Multimodal big model research and learning (updated)

Development and future prospects of multimodal large language model

Table of contents

1. introduction
2. Historical development
3. Current status
4. Technical Architecture
5. Application scenarios
6. Challenges and limitations
7. Future trends
8. in conclusion
9. References

introduction

The field of artificial intelligence is undergoing unprecedented changes, and the multimodal Large Language Models (MLLMs) are the core driver of this change, reshaping the way we interact with technology. Unlike traditional models that can only process a single type of data, the multimodal large language model can simultaneously understand and generate various forms of information such as text, images, audio, and video, bringing artificial intelligence a closer ability to human cognition.

This research report aims to comprehensively and in-depth discussion of the development history, current status, technical architecture, application scenarios, challenges faced and future development trends of multimodal large language models. Through systematic analysis and research, we hope to provide readers with a shallow to deep perspective to understand the full picture of this cutting-edge technology and its profound impact on future society.

Research background and significance

The essence of human cognition and communication is multimodal. We obtain information through various senses such as vision, hearing, touch, and express our thoughts through language, expressions, and body movements. Traditional artificial intelligence systems are often limited to a single mode and cannot fully simulate human cognitive processes. The emergence of multimodal large language model marks an important step towards artificial intelligence being closer to human cognition.

The research and development of multimodal large language model has important theoretical and practical significance:

1. Theoretical significance: The research on multimodal large language model has promoted the development of basic theory of artificial intelligence, especially important breakthroughs in modal fusion, cross-modal learning, representation learning, etc., laying the foundation for the realization of general artificial intelligence (AGI).

2. Technical significance: The multimodal large language model integrates technical achievements in multiple fields such as computer vision, natural language processing, and speech recognition, promotes the integration and innovation of technologies in various fields, and promotes the overall progress of artificial intelligence technology.

3. Application Meaning: The multimodal large language model can process and understand more complex and richer information, providing more powerful intelligent tools for all walks of life and creating new application scenarios and business value.

4. Social significance: The multimodal large language model is expected to improve human-computer interaction experience, improve information acquisition and processing efficiency, promote knowledge dissemination and innovation, and provide new ideas and methods for solving social problems.

This research report will in-depth discussion of multimodal large language model from multiple dimensions, providing readers with a comprehensive and systematic knowledge framework to help understand the development context, core principles and future direction of this cutting-edge technology.

Research methods and content overview

This study uses a combination of literature research, case analysis and trend prediction to comprehensively collect and analyze academic papers, technical reports, industry trends and application cases related to multimodal large language models, striving to provide objective, comprehensive and in-depth analysis and insights.

The report includes the following main parts:

1. Historical development: Trace back to the origin and evolution of the multimodal large language model, and sort out key technological breakthroughs and milestone events.

2. Current status: Analyze the performance indicators, advantages and disadvantages and applicable scenarios of mainstream multimodal large language models, and evaluate the maturity and limitations of current technology.

3. Technical Architecture: In-depth discussion of the basic principles, architectural design, training methods and key technologies of multimodal large language model, and reveal its internal working mechanism.

4. Application scenarios: Comprehensively sort out the application cases and potential value of multimodal large language models in various industries and fields, and demonstrate their actual effects and impact.

5. Challenges and limitations: Analyze the technical challenges, ethical problems and social impacts faced by multimodal large language models, and explore possible solutions and coping strategies.

6. Future trends: Based on the current development trend, predict the future development direction and potential breakthroughs of multimodal large language models, and look forward to its long-term impact and value.

Through this series of contents, this report aims to provide readers with a comprehensive knowledge framework for understanding multimodal large language models, helping researchers, developers, decision makers and people from all walks of life who are concerned about the development of artificial intelligence to grasp the essence and future of this cutting-edge technology.

Historical development

The development of multimodal large language model can be traced back to the cross-fusion of computer vision and natural language processing. In the early stages of artificial intelligence research, researchers began to explore how computers can understand two different information modalities, images and text at the same time.

The Origin of Early Multimodal Systems (1970s-2000s)

The earliest attempts in multimodal research can be traced back to the 1970s. At that time, researchers began to explore how to associate images with text, but due to the limitations of computing power and algorithms, these attempts mainly stayed in the proof-of-concept stage.

In 1979, Nicholas Negroponte proposed the concept of "media convergence" at the MIT Media Laboratory, foreseeing the tendency of different media forms (text, images, audio, etc.) to be integrated in a digital environment, which can be regarded as the theoretical starting point for multimodal research.

In the 1990s and early 2000s, with the development of computer vision and natural language processing, researchers began to try to build simple systems that can process images and text. These systems usually adopt modular design, i.e. using specialized models to process data from different modalities, and then combine the results through simple rules or statistical methods.

The emergence of early multimodal tasks (2000s-2010s)

From the mid-2000s to the early 2010s, some specific multimodal tasks began to emerge and attracted the attention of researchers:

1. Image description generation: In 2006, researchers began to explore how to automatically generate descriptive text for images. Early methods were mainly based on templates and rules, by identifying objects and relationships in images and then filling in predefined sentence templates.

2. Visual Q&A (VQA): Around 2010, researchers began to study how to get computers to answer questions about image content. Early VQA systems usually process image recognition and natural language processing as independent steps.

3. Cross-modal search: During this period, cross-modal retrieval research also occurred, that is, query of one modal (such as text) is used to retrieve the content of another modal (such as images).

Although these early multimodal systems were limited in functionality, they laid the foundation for later development, especially in the definition of problem, evaluation methods and the establishment of benchmark data sets.

Evolution from single mode to multimodal

The development of multimodal large language model has gone through a long evolution process from singlemodal model to multimodal fusion, which is closely related to the development of deep learning technology.

Deep Learning Revolution and the Rise of Single-Modal Models (2012-2018)

In 2012, AlexNet's success in the ImageNet competition marked a breakthrough in deep learning in the field of computer vision. In the following years, deep learning technology has made a series of important progress in the fields of computer vision and natural language processing:

1. Computer Vision Field: The emergence of network architectures from AlexNet to VGG, GoogLeNet, ResNet and other network architectures has greatly improved the accuracy of image recognition.

2. Natural Language Processing Field: From word embedding technologies such as Word2Vec and GloVe, to recurrent neural networks such as LSTM and GRU, to the proposal of Transformer architecture in 2017, natural language processing capabilities have been continuously improved.

During this period, although the single-modal model has made significant progress, multimodal systems still mainly adopt the "late fusion" method, that is, use special models to process data from different modes, and then fusion is carried out at the decision-making level.

Early multimodal deep learning model (2015-2019)

As deep learning technology matures, researchers have begun to explore how to use deep neural networks to build more integrated multimodal systems:

1. Show and Tell（2015）: The image description generation model proposed by Google's research team, using CNN to extract image features and then using RNN to generate description text, is a representative of the early end-to-end training multimodal model.

2. VQA Model (2016-2018): A series of visual question and answer models have been proposed, such as Stacked Attention Networks, Bottom-Up and Top-Down Attention, etc. These models usually use attention mechanisms to correlate image areas and words in the problem.

3. CLIP (Research and development started in 2018): OpenAI began to develop the CLIP (Contrastive Language-Image Pre-training) model. Although it was not officially released until 2021, its R&D work began during this period.

Although these early multimodal deep learning models achieved decent performance on specific tasks, they were usually designed for a single task and lacked versatility and flexibility.

Key technologies breakthroughs and milestone events

In the development process of multimodal large language model, there are several key technological breakthroughs and milestone events that deserve special attention.

The Rise of Pre-trained Models (2018-2020)

The rise of pre-trained models is an important development in the fields of natural language processing and computer vision, and has laid the foundation for multimodal large language models:

1. BERT（2018）: The bidirectional Transformer encoder proposed by Google has significantly improved the performance of various natural language processing tasks through large-scale unsupervised pre-training.

2. GPT Series (2018-2020): The generative pre-trained Transformer model released by OpenAI, especially GPT-2 and GPT-3, demonstrates the powerful capabilities of large-scale language models.

3. Self-supervised visual pre-training: The proposal of self-supervised learning methods such as SimCLR and MoCo makes it possible to pre-train visual models on label-free data.

The success of these pre-trained models provides a technical basis and idea for multimodal pre-training.

The emergence of multimodal pretrained models (2019-2021)

From 2019 to 2021, multimodal pre-trained models began to appear, marking the initial formation of multimodal large language models:

1. ViLBERT and LXMERT (2019): These models extend BERT's pre-training approach to the vision-language domain, learning a joint representation of vision and language by pre-training on large-scale image-text pair data.

2. CLIP（2021）: The contrast learning image-text pre-trained model officially released by OpenAI, which learns powerful visual-language alignment representation through training on 400 million images-text pairs, and can migrate zero samples to various visual tasks.

3. DALL-E（2021）: The text-to-image generation model published by OpenAI can generate corresponding images based on text descriptions, demonstrating the potential of multimodal generation.

Although these models are not multimodal large language models in the full sense, they have made important progress in the joint understanding and generation of vision and language, laying the foundation for subsequent development.

The Rise of Multimodal Large Language Model (2022-2025)

Since 2022, with the rapid development of large language model technology, the true multimodal large language model has begun to appear:

1. Flamingo（2022）: The visual-language model released by DeepMind is able to process mixed inputs of images and text and generate corresponding text outputs. It is a representative of the early multimodal large language model.

2. GPT-4V（2023）: The GPT-4 Vision version released by OpenAI extends the capabilities of GPT-4 to the visual field, able to understand and analyze images, and generate relevant text.

3. Claude 3 Opus（2023-2024）: Anthropic's multimodal large language model has performed well in visual comprehension and text generation.

4. Gemini（2023-2024）: A multimodal large language model released by Google, which can handle inputs in multiple modalities such as text, images, audio and video.

5. GPT-4o（2024）: The multimodal large language model released by OpenAI further improves visual comprehension and response speed compared to GPT-4V.

These models mark the formal rise of multimodal large language models, which not only understand inputs from multiple modalities, but also generate coherent, relevant text outputs, demonstrating powerful cross-modal understanding and generation capabilities.

Contributions of major research institutions and enterprises

The development of multimodal large language models cannot be separated from the contribution of various research institutions and enterprises, which have promoted the rapid development of this field through technological innovation and resource investment.

Academic research institutions

1. Stanford University: Important contributions have been made in the cross-research of computer vision and natural language processing, such as the establishment of ImageNet datasets and early research on image description generation.

2. Carnegie Mellon University: In-depth research on the theory and methods of multimodal machine learning, and an important framework for multimodal representation learning has been proposed.

3. MIT: It has made important contributions to visual-language pre-training and multimodal fusion, and has developed multiple influential multimodal data sets and models.

4. University of California, Berkeley: Has deep accumulation in the fields of computer vision and deep learning, and has made important contributions to vision-language models.

Industrial Research Laboratory

1. OpenAI: Important multimodal models such as CLIP, DALL-E, GPT-4V and GPT-4o have been developed, which has promoted the development of large-scale multimodal pre-training.

2. Google/DeepMind: Developed multimodal large language models such as Flamingo, PaLM-E, and Gemini, and made important contributions to multimodal fusion and understanding.

3. Meta AI (formerly Facebook AI Research): In-depth research on multimodal pre-training and understanding, and multiple open source multimodal models and data sets have been developed.

4. Microsoft Research: It has made important contributions to visual-language pre-training and multimodal applications, and has developed multiple influential multimodal models.

5. Anthropic: The Claude series multimodal large language model has been developed, which has made unique contributions in safe alignment and multimodal understanding.

Chinese enterprises and research institutions

1. Baidu: The Wenxin Yiyan multimodal model has been developed, which has made important contributions to the understanding and generation of Chinese multimodals.

2. Alibaba Damo Academy: In-depth research has been conducted in multimodal pre-training and application, and multimodal models such as Tongyi Qianwen have been developed.

3. Tencent AI Lab: Make important contributions to multimodal understanding and generation, and multiple multimodal pre-trained models have been developed.

4. Smart AI: The Zhishu GLM series multimodal large language model has been developed, and has made unique contributions to the understanding of Chinese multimodal.

5. Tsinghua University: In-depth research has been conducted in multimodal representation learning and pre-training, and multiple influential multimodal models have been developed.

These research institutions and enterprises have jointly promoted the development of multimodal large language models by publishing papers, open source code and models, organizing competitions and seminars. Their contributions not only include technological innovation, but also include data set construction, evaluation method formulation and application scenario exploration.

The evolution route of multimodal large language model

Looking at the development history of multimodal large language model, the following main evolution routes can be summarized:

From modular to end to end

Early multimodal systems usually adopted modular designs, that is, using specialized models to process data from different modalities, and then combined the results through simple rules or statistical methods. With the development of deep learning technology, multimodal systems are gradually developing towards end-to-end training, that is, processing data of multiple modalities simultaneously in a unified framework, and improving overall performance through joint optimization.

From task-specific to universal pre-training

Early multimodal models were usually designed for specific tasks, such as image description generation, visual question and answer, etc. With the rise of the pre-training paradigm, multimodal models began to adopt large-scale pre-training and fine-tuning methods. By pre-training on a large number of unlabeled or weakly labeled data, they learned general multimodal representations, and then fine-tuned on specific tasks, greatly improving the universality and migration capabilities of the model.

From dual mode to multi-modal

Early research mainly focused on the visual-language pair modes, such as image-text, video-text, etc. With the development of technology, researchers have begun to explore more modal fusions, such as vision-language-audio, vision-language-tactile, etc., moving towards a true multimodal system.

From understanding to generation

Early multimodal models focused mainly on understanding tasks, such as image classification, visual question and answer, etc. With the development of generative model technology, multimodal generation tasks have begun to attract attention, such as text-to-image generation, image-to-text generation, etc., demonstrating the potential of multimodal models in creative content generation.

From shallow fusion to deep fusion

Early multimodal fusion usually uses shallow methods, such as feature splicing, weighted average, etc. With the development of attention mechanisms and Transformer architecture, multimodal fusion has begun to adopt deeper methods, such as cross attention, multi-head attention, etc., which can capture more complex interactive relationships between different modes.

From closed systems to open world

Early multimodal models were usually trained and evaluated on enclosed datasets and tasks, with limited performance. With the development of large-scale pre-training and zero-sample learning techniques, multimodal models have begun to demonstrate the ability to understand and generate content in the open world, such as CLIP being able to migrate zero-sample to new visual classification tasks, and GPT-4V being able to understand and describe various real-world images.

These evolutionary routes reflect the technological development trend of multimodal large language models and also indicate possible future research directions. With the improvement of computing power, the expansion of data scale and the innovation of algorithms, multimodal large language models are expected to make greater breakthroughs in these directions and move towards true general artificial intelligence.

Current status

Multimodal large language models (MLLMs) have become the cutting-edge research direction in the field of artificial intelligence, and major technology companies and research institutions have launched their own multimodal large language models. This section will provide a comprehensive overview of mainstream multimodal large language models and analyze their characteristics, performance and applicable scenarios.

Overview of mainstream multimodal large language models

International mainstream multimodal large language model

GPT-4V/GPT-4o（OpenAI）

GPT-4V (Vision) is a multimodal large language model launched by OpenAI in 2023 and is a visually enhanced version of GPT-4. In May 2024, OpenAI further launched GPT-4o ("o" stands for "omni", which means "all-round"), which is a more advanced multimodal model.

Main features：

• Ability to process and understand images, text input, and generate text output
• Have strong visual understanding ability, can analyze charts, recognize text, and understand image content
• Compared with GPT-4V, GPT-4o has faster response speed and stronger multimodal understanding capabilities.
• Supports real-time voice interaction, can understand user's voice input and generate voice output

Performance metrics：

• Excellent performance in multiple visual understanding benchmarks, such as VQAv2, TextVQA, etc.
• Outstanding performance in understanding and analysis of complex charts
• Show powerful capabilities in cross-modal inference tasks

Applicable scenarios：

• Image content analysis and description
• Document understanding and question and answer
• Visually assisted decision making
• Creative content generation
• Education and training

Claude 3 Series (Anthropic)

Anthropic launched the Claude 3 series multimodal large language models in 2024, including three versions: Claude 3 Haiku, Claude 3 Sonnet and Claude 3 Opus, among which Opus is the most powerful version.

Main features：

• Ability to process text and image input and generate text output
• Excellent performance in visual understanding, especially in detail recognition and analysis
• Emphasize safety and alignment to reduce harmful output and hallucinations
• Have strong contextual understanding ability, able to handle long texts and complex instructions

Performance metrics：

• Claude 3 Opus surpassed GPT-4 in multiple assessments, including GRE, LSAT and other exams
• Excellent performance in visual comprehension tasks, especially in detail recognition and document analysis
• Maintain high-quality output in multiple rounds of dialogue and complex reasoning tasks

Applicable scenarios：

• Complex document analysis
• Academic research aid
• Content creation and editing
• Professional field consultation (such as law, medical care)
• Education and training

Gemini series (Google)

Google launched the Gemini series multimodal large language model at the end of 2023, including three versions: Gemini Ultra, Gemini Pro and Gemini Nano, among which Ultra is the most powerful version. In 2024, Google further launched the Gemini 1.5 series, bringing stronger multimodal capabilities and longer context windows.

Main features：

• Native multimodal design, integrating text, image, audio and video capabilities from the beginning of training
• Have strong multimodal reasoning ability and be able to understand the relationship between different modes
• Gemini 1.5 supports ultra-long context windows (up to 1 million tokens), capable of handling long documents and multiple images
• Provides versions of different sizes to adapt to different deployment environments, from the cloud to mobile devices

Performance metrics：

• Gemini Ultra leads in benchmarks such as MMLU (Massive Multitasking Language Understanding)
• Excellent performance in multimodal benchmark tests, such as multimodal inference, video understanding, etc.
• Gemini 1.5 has significant advantages in long context understanding and processing

Applicable scenarios：

• Complex multimodal content understanding
• Long document analysis and summary
• Video content understanding and description
• Scientific research and data analysis
• Creative content generation

DALL-E 3（OpenAI）

DALL-E 3 is a text-to-image generation model launched by OpenAI in 2023 and is the latest version of the DALL-E series. While it focuses primarily on image generation rather than a comprehensive multimodal understanding, it represents an important advance in the field of multimodal generation.

Main features：

• Ability to generate high-quality, high-resolution images based on detailed text descriptions
• Integrated with ChatGPT, users can improve image generation requirements through dialogue-based interactions
• Able to understand complex text prompts, including scene description, style requirements, composition guidance, etc.
• Have strong creative understanding ability and be able to visualize abstract concepts

Performance metrics：

• Significant improvements in image quality, text alignment and creative expression
• Ability to generate images that are more in line with user intentions, reducing misunderstandings and biases
• Excellent performance in art style simulation and detail performance

Applicable scenarios：

• Creative design and artistic creation
• Marketing and advertising content generation
• Product concept visualization
• Educational content production
• Entertainment and game resource generation

Midjourney

Midjourney is an AI system focusing on text-to-image generation. Although it is not a multimodal large language model in the traditional sense, its achievements in the field of image generation make it an important representative of multimodal AI.

Main features：

• Ability to generate highly artistic and visually impactful images based on text prompts
• Supports advanced features such as style mixing, reference image and detail control
• Provide services through the Discord platform to form an active creator community
• Continuous iterative updates, continuously improving image quality and generation capabilities

Performance metrics：

• Outstanding performance in artistic and aesthetic quality
• Ability to generate highly detailed and rich textured images
• Unique advantages in creative expression and style diversity

Applicable scenarios：

• Art creation and illustration design
• Conceptual Art and Visual Development
• Marketing and Brand Visual Content
• Personal creative projects
• Entertainment and media content production

Mainstream multimodal large language model in China

Wen Xin Yiyan (Baidu)

Wen Xin Yiyan is a multimodal large language model launched by Baidu in 2023, and is one of the earliest multimodal large models published in China.

Main features：

• Supports input and understanding of various modalities such as text, images, and voice
• Have the advantages of Chinese understanding and generation, and have a deep understanding of Chinese context and culture
• Provide rich APIs and application scenarios to support enterprise-level application development
• Continuous iterative updates, continuously enhancing multimodal understanding and generation capabilities

Performance metrics：

• Excellent performance in Chinese multimodal comprehension task
• Strong ability in knowledge Q&A and creative writing
• Continuous improvement in image understanding and description

Applicable scenarios：

• Intelligent customer service and dialogue system
• Content creation and editing
• Educational training and knowledge services
• Enterprise application development
• Cultural and creative industry

Tongyi Qianwen (Alibaba)

Tongyi Qianwen is a multimodal large language model launched by Alibaba Damo Academy in 2023, with strong multimodal understanding and generation capabilities.

Main features：

• Supports text and image input, and can generate text output
• Featured optimization in vertical fields such as e-commerce and medical care
• Have strong knowledge base and reasoning skills
• Provide open platform and API services to support application development

Performance metrics：

• Excellent in Chinese understanding and generation
• Have distinctive advantages in the application of knowledge in vertical fields
• Outstanding competence in multi-round dialogue and contextual understanding

Applicable scenarios：

• E-commerce smart assistant
• Medical and health consultation
• Educational and training services
• Content creation and editing
• Enterprise knowledge management

Spark Cognition (iFlytek)

Spark Cognition is a multimodal large language model launched by iFLYTEK, combining iFLYTEK's advantages in voice technology.

Main features：

• Supports multiple modal inputs such as text, images, and voice
• Unique advantages in voice interaction
• In-depth optimization in vertical fields such as education and medical care
• Focus on knowledge security and content reliability

Performance metrics：

• Excellent in speech recognition and understanding
• Highly accurate knowledge in areas such as education and medical care
• Good performance in multi-round dialogue fluency

Applicable scenarios：

• Intelligent education application
• Medical and health services
• Smart voice assistant
• Government and corporate services
• Content creation and editing

Zhipu GLM (Zhipu AI/Tsinghua University)

Zhipu GLM is a multimodal large language model series jointly developed by Zhipu AI and Tsinghua University, including ChatGLM and CogVLM.

Main features：

• Open source and open technology route, providing model versions of multiple scales
• Advantages in Chinese understanding and generation
• Low computing resource requirements, support local deployment
• Balance between academic research and industrial applications

Performance metrics：

• Excellent performance under resource constraints
• Good performance in Chinese multimodal comprehension task
• Get extensive application and optimization in the open source community

Applicable scenarios：

• Academic Research and Education
• Small and medium-sized enterprise application development
• Personalized customization service
• Local deployment scenario
• Privacy-sensitive applications

Performance indicators and evaluation methods

Evaluating the performance of multimodal large language models is a complex task that requires consideration of multiple dimensions and metrics. This section will introduce the current mainstream evaluation methods and performance metrics.

Benchmarks and datasets

Vision-Language Understanding Benchmark

1. VQA（Visual Question Answering）: Evaluate the model's ability to answer questions about images. Commonly used data sets include VQAv2, OK-VQA, etc.

2. NLVR2（Natural Language for Visual Reasoning）: Evaluate the model's ability to reason about images based on natural language description.

3. Visual Entailment: Evaluate the model's ability to judge whether the text description is consistent with the image content.

4. TextVQA: Focus on evaluating the model's ability to understand the text content in the image and answer related questions.

5. DocVQA: Evaluate the model's ability to understand document images and answer questions, focusing on document understanding.

Multimodal generation benchmark

1. MS COCO Captions: Evaluate the quality of the image description generated by the model, and use BLEU, METEOR, CIDEr and other indicators.

2. Flickr30k: Another dataset that evaluates the ability to generate image descriptions.

3. DALL-E Benchmark: Evaluate text-to-image generation quality and text alignment.

Comprehensive ability assessment

1. MMMU（Massive Multi-discipline Multimodal Understanding）: Evaluate the performance of the model in multidisciplinary multimodal understanding tasks.

2. MME（Multimodal Evaluation）: Comprehensively evaluate the capabilities of multimodal models in perception, cognition and reasoning.

3. MM-Bench: Comprehensive benchmarking of multimodal models, covering a variety of tasks and capability dimensions.

Evaluation indicators

Accuracy indicators

1. Accuracy: Correct predicted ratio, often used in classification tasks.

2. F1 score: The harmonic average of precision and recall, suitable for unbalanced datasets.

3. BLEU/ROUGE/METEOR/CIDEr: Evaluate the similarity between the generated text and the reference text, and is often used in image description tasks.

4. FID（Fréchet Inception Distance）: Evaluate the similarity between the generated image and the real image distribution.

5. CLIP Score: Use the CLIP model to evaluate the alignment of the generated images with the text prompts.

Human Assessment Indicators

1. Human preference rating: Let human evaluators compare the output qualities of different models.

2. Turing Test: Evaluate whether the model output can be distinguished from human output.

3. Task completion degree: Evaluate whether the model successfully completes the specified task.

4. User satisfaction: Evaluate the user's satisfaction with model output.

Multimodal capability dimension

When evaluating multimodal large language models, the following dimensions are usually considered:

1. Cross-modal understanding: The model's ability to understand the relationships between different modalities.

2. Visual perception: The ability to recognize and understand elements such as objects, scenes, texts, etc. in an image.

3. Visual reasoning: The ability to conduct logical reasoning based on visual information.

4. Knowledge application: The ability to apply existing knowledge to multimodal understanding tasks.

5. Creative Generation: The ability to generate innovative and diversified content.

6. Follow the instructions: The ability to execute tasks according to user instructions.

7. robustness: Processing capability for noise, fuzzy or incomplete inputs.

Model comparison and applicable scenario analysis

Different multimodal large language models have their own advantages and disadvantages in all aspects and are suitable for different application scenarios. This section will conduct a comparative analysis of mainstream models and explore their best applicable scenarios.

Performance comparison

Comparison of visual comprehension skills

In terms of visual understanding, GPT-4V/GPT-4o, Claude 3 Opus and Gemini Ultra are most prominent, which understand complex images, analyze charts and identify details. in:

• GPT-4V/GPT-4o: Best performing in chart understanding and document analysis, able to accurately extract chart data and analyze it.
• Claude 3 Opus: Excellent in detail recognition and description, and has strong perception of subtle elements in the image.
• Gemini Ultra: It has advantages in understanding complex scenes and video content analysis, and can understand timing information.

In Chinese models, Wen Xin Yiyan and Tongyi Qianwen performed well in Chinese image understanding, especially in Chinese documents and graph analysis.

Comparison of multimodal reasoning capabilities

In terms of multimodal reasoning, each model is as follows:

• GPT-4V/GPT-4o: Best performing in cross-modal reasoning and knowledge application, able to combine image information and background knowledge to conduct complex reasoning.
• Claude 3 Opus: Excellent in logical reasoning and consistency, and the reasoning process is more transparent and explainable.
• Gemini Ultra: Have advantages in scientific reasoning and mathematical problem solving, and be able to understand and analyze scientific charts and data.

In the Chinese model, Zhipu GLM has strong reasoning ability in the academic and scientific fields, while Wen Xinyiyan has outstanding reasoning ability in the Chinese culture and social fields.

Generation ability comparison

In terms of content generation:

• DALL-E 3: The best performance in text-to-image generation, the generated image quality is high, and the alignment with text description is good.
• Midjourney: Leading in artistic and creative expression, the generated images have a unique artistic style and visual impact.
• GPT-4o: The text generation ability based on multimodal content understanding is the strongest, and can generate coherent, relevant, and informative text.

In the Chinese model, Wen Xin Yiyan performed well in Chinese creative writing and content generation, while Tongyi Qianwen has advantages in content generation in professional fields.

Applicable scenario analysis

Enterprise application scenarios

1. Customer Service and Support

   • Best suitable models: Claude 3 series, GPT-4o, Wen Xinyiyan
   • Advantages: Strong multi-round dialogue ability, good context understanding, and ability to process images and documents uploaded by customers

2. Content creation and marketing

   • Best suitable models: GPT-4o, DALL-E 3, Midjourney, Tongyi Qianwen
   • Advantages: Strong creative generation ability, able to generate various forms of content to meet different marketing needs

3. Data analysis and decision support

   • Best suitable models: GPT-4V, Gemini Ultra, Claude 3 Opus
   • Advantages: Strong chart understanding and data analysis capabilities, ability to extract key information and reason

4. Knowledge Management and Retrieval

   • Best suitable model: Claude 3 series, Gemini 1.5, Wen Xin Yiyan
   • Advantages: Strong long context processing capability, rich knowledge base, and high retrieval accuracy

Vertical industry applications

1. Medical Health

   • Best suitable model: Claude 3 Opus, Spark Cognition, Tongyi Qianwen Medical Edition
   • Advantages: High professional knowledge and high accuracy, strong medical image understanding ability, and focus on security and privacy protection

2. Educational training

   • The most suitable model: GPT-4o, Xinghuo Cognitive Education Edition, Wenxin Yiyan
   • Advantages: Strong understanding of multimodal teaching content, able to provide personalized learning support, good interaction

3. Financial Services

   • Best suitable models: GPT-4V, Claude 3 Opus, Tongyi Qianwen
   • Advantages: Strong financial documents and chart analysis capabilities, high inference accuracy, and good security

4. Manufacturing and Industry

   • Best suitable model: Gemini Ultra, Wenxin Yiyan Industrial Edition
   • Advantages: Strong industrial image and data comprehension capabilities, supporting applications in multiple industrial scenarios

Creative and Entertainment Applications

1. Art creation

   • Best suited models: Midjourney, DALL-E 3
   • Advantages: Strong artistic expression, diverse creativity, and high visual quality

2. Game development

   • Best suitable models: GPT-4o, DALL-E 3, Gemini Ultra
   • Advantages: Ability to generate game materials, plots and dialogues, and support interactive content creation

3. Media and Publishing

   • Best suitable models: GPT-4o, Claude 3 Opus, Wen Xin Yiyan
   • Advantages: Strong content creation ability, able to understand and generate multiple media forms, and support editing workflow

Personal use scenarios

1. Study and research

   • Best suitable models: Claude 3 Opus, GPT-4o, Zhishu GLM
   • Advantages: High knowledge accuracy, strong interpretation ability, and support deep learning and research

2. Creative Assistance

   • Best suitable models: DALL-E 3, Midjourney, GPT-4o
   • Advantages: Strong creative generation ability, supports multiple creative expression forms, good interaction

3. Daily Assistant

   • Best suitable models: GPT-4o, Gemini Pro, Wen Xinyiyan
   • Advantages: comprehensive and comprehensive, fast response speed, high user-friendliness

Current status of commercial application

The commercial application of multimodal large language models is developing rapidly, and major companies adopt different business models and strategies to promote the implementation of these technologies.

Business model and pricing strategy

Subscription Mode

Most multimodal large language models adopt a subscription-based business model and provide services at different levels:

• OpenAI: Provides subscription services at different levels such as ChatGPT Plus ($20 per month) and ChatGPT Team/Enterprise. Advanced subscriptions can access multimodal capabilities such as GPT-4o.
• Anthropic: Provide subscription services such as Claude Pro ($20 per month) and Claude Team/Enterprise, with different usage restrictions and features at different levels.
• Midjourney: Offers subscriptions at different levels of basic ($10 per month) to professional ($60 per month) and are priced based on the quantity and quality of generated images.

API service model

Many companies offer API services that allow developers to integrate multimodal capabilities into their applications:

• OpenAI: Provides API services for GPT-4V/GPT-4o and DALL-E 3, billed according to usage.
• Google: Provides Gemini API, including model versions of different sizes, billed by API calls and compute resource usage.
• Baidu: Provide Wenxin Yiyan API service, supporting customization of different packages according to call volume and QPS requirements.

Enterprise Solutions

For enterprise customers, multimodal large language model providers have developed customized solutions:

• Private enterprise deployment: Allows enterprises to deploy models on their own infrastructure to ensure data security and privacy.
• Industry custom model: Model version optimized for specific industries (such as medical, finance, law, etc.).
• Integrated Services: Provide technical consulting, system integration and customized development services to help enterprises make full use of multimodal AI capabilities.

Industry application cases

Retail and e-commerce

1. Virtual fitting and product display: Use multimodal models to generate product images in different scenarios to provide a virtual fitting experience.

   • Case: Alibaba uses virtual modeling technology supported by Tongyi Qianwen, allowing consumers to "try on" clothes on different models.

2. Smart customer service and shopping assistant: Combining image recognition and natural language processing to provide a smarter shopping experience.

   • Case: JD.com uses multimodal AI technology to develop intelligent customer service, which can understand the product pictures uploaded by users and provide relevant suggestions.

Medical Health

1. Medical imaging-assisted diagnosis: Combining medical imaging and clinical texts, assisting doctors in diagnosis.

   • Case: Tencent Miying uses multimodal AI technology to assist doctors in analyzing medical images such as CT and MRI to improve diagnostic efficiency and accuracy.

2. Doctor-patient communication assistance: Help doctors explain complex medical concepts and examination results.

   • Case: Ping An Good Doctor uses multimodal AI technology to help doctors explain medical images and examination reports to patients.

Educational training

1. Intelligent teaching assistant: Understand the assignments submitted by students (including images, text, etc.) and provide feedback.

   • Case: iFLYTEK's Spark Cognitive Education Edition, which can understand pictures of students' handwriting homework and provides personalized tutoring.

2. Multimedia learning content generation: Automatically generate teaching materials, including handouts and exercises with pictures and texts.

   • Case: Homework Help uses multimodal AI technology to automatically generate pictures based on the teaching syllabus.

Financial Services

1. Document automation processing: Understand and extract key information from financial documents (such as contracts, reports, etc.).

   • Case: Ping An Bank uses multimodal AI technology to automatically process loan application documents to improve approval efficiency.

2. Risk Assessment and Fraud Detection: Analyze multiple data sources (including images, text, etc.) to identify potential risks.

   • Case: Ant Financial uses multimodal AI technology to analyze transaction data and user behavior to improve the accuracy of fraud detection.

The development status of the open source community

The open source multimodal large language model plays an important role in promoting technological democratization and innovation.

Main open source multimodal model

1. LLaVA（Large Language and Vision Assistant）: Open source multimodal model developed by Stanford University and Microsoft Research, combining open source LLM and visual encoder.

2. MiniGPT-4: A lightweight multimodal model developed by King Abdullah University of Science and Technology aims to reproduce some of the multimodal capabilities of GPT-4.

3. Zhipu GLM series: Open source multimodal model jointly developed by Zhipu AI and Tsinghua University, including ChatGLM and CogVLM.

4. BLIP-2: An open source vision-language model developed by Salesforce Research, using a lightweight query converter to connect vision models and LLM.

5. VisualGLM: An open source multimodal dialogue model based on ChatGLM and EVA, supporting multimodal dialogue in Chinese and English.

Open Source Community Contribution

The contribution of the open source community in the field of multimodal large language models is mainly reflected in the following aspects:

1. Model optimization and improvement: Community developers continuously optimize the performance of open source models, improve inference efficiency, and reduce resource requirements.

2. Dataset construction: Create and share high-quality multimodal data sets, such as LAION-5B, CC12M, etc.

3. Tools and framework development: Develop tools and frameworks that support multimodal model training and deployment, such as Hugging Face's Transformers library.

4. Application examples and tutorials: Share application examples and tutorials of multimodal models to lower the threshold for use.

5. Model evaluation and benchmarking: Establish fair and comprehensive assessment methods and benchmarks to promote technological progress.

The relationship between open source and business model

A complementary relationship is formed between open source and commercial multimodal models:

1. Technology communication and innovation: The open source model promotes the dissemination and innovation of technology and promotes the development of the entire field.

2. Differentiated positioning: Open source models usually focus on specific capabilities or application scenarios, while business models pursue comprehensive capabilities and service quality.

3. Resource complementary: Commercial companies provide computing resources and funds to support open source projects, and open source communities provide innovative ideas and talents.

4. Application Ecology: The open source model provides small and medium-sized enterprises and individual developers with the opportunity to enter the multimodal AI field and enriches the application ecosystem.

The current state of multimodal large language model demonstrates the booming development and great potential of this technology field. With the continuous advancement of technology and the continuous expansion of applications, multimodal large language models will play an increasingly important role in the field of artificial intelligence and bring profound changes to all walks of life.

Technical Architecture

The architectural design of the multimodal large language model (MLLM) is the key to achieving cross-modal understanding and generation. Although different models differ in specific implementations, most multimodal large language models follow a basic architectural framework, usually consisting of three core modules.

Basic architecture overview

Core architecture components

1. Multimodal Encoder：

   • Responsible for receiving and effectively encoding input data from different modes (such as images, text, audio, etc.)
   • Convert raw data from different modes into feature representations that can be processed by neural networks
   • Usually includes pretrained encoders specific to each modal, such as visual encoders, text encoders, etc.

2. Multimodal Projector：

   • Achieve data alignment and fusion between different modes
   • Map features of different modalities to a shared semantic space
   • Ensure that information from different modalities can be effectively interacted and integrated

3. Large Language Model：

   • Receive aligned multimodal signals and perform inference and generation
   • Usually based on Transformer architecture, it has strong context understanding and generation capabilities
   • As the "brain" of the entire system, it is responsible for the final decision-making and output generation

This architectural design allows the model to process information from different modalities, understand and generate in a unified semantic space, thereby achieving intelligent interaction across modalities.

Typical architecture examples

Here are several typical multimodal large language model architecture examples:

LLaVA architecture

LLaVA (Large Language and Vision Assistant) adopts a simple and effective architecture:

• Extract image features using a pre-trained visual encoder such as CLIP ViT
• Map visual features to the embedding space of the language model through a linear projection layer
• Embed and splice the projected visual features with text and input them into a large language model for processing

BLIP-2 architecture

BLIP-2 adopts a more complex Q-Former architecture:

• Extract image features using a pretrained visual encoder
• Extract key information from visual features through Q-Former (a set of learnable query vectors)
• The output of Q-Former is mapped to the embed space of the language model through a projection layer
• Finally, the mapped features are sent to the large language model together with the text input

Flamingo architecture

Flamingo adopts a Perceiver Resampler architecture:

• Extract image or video features using a pretrained visual encoder
• Convert variable-length visual features into a fixed number of visual tokens through perceptual resampler
• Fusion of visual and linguistic information in the cross-attention layer of language model
• Use frozen language models as the basis to train only the newly added cross-attention layer

These different architectural designs reflect different strategies and tradeoffs for multimodal fusion, each with its unique advantages and applicable scenarios.

The basic principles of multimodal fusion

Multimodal fusion is the core technology of multimodal large language model, which determines how the model integrates information from different modes. According to the timing and method of fusion, multimodal fusion can be divided into the following types:

Early Fusion

Early fusion is the fusion of raw data or low-level features of different modalities at the early stages of feature extraction.

How it works：

• Data from different modes are directly combined at the initial stage of input layer or feature extraction
• Usually achieved through simple splicing, weighted summing or tensor product.
• The fused features are processed together through the subsequent neural network layer.

advantage：

• Ability to capture low-level correlations between modals
• Models can learn deeper cross-modal representations from the beginning
• The architecture is relatively simple, and the training process is more direct

shortcoming：

• The data formats and dimensions of different modes vary greatly, and it is difficult to directly fusion.
• May cause information loss or increase noise
• High requirements for data preprocessing and alignment

Application Cases：

• Some early multimodal classification models
• Simple audio and video fusion system

Middle Fusion

Medium-term fusion is performed at the intermediate level after the feature extraction of each mode to a certain extent.

How it works：

• Each modal first extracts intermediate features through its respective encoder
• Integrate features using attention mechanisms or other fusion methods at the middle layer of the network
• The converged features continue to be processed through the shared network layer

advantage：

• Retains specific features of each modal
• Ability to learn more complex intermodal interactions
• Balances modal specific information and cross-modal information

shortcoming：

• Complex fusion mechanisms need to be designed
• There may be a problem with inter-modal alignment
• High computational complexity

Application Cases：

• Some variations of the CLIP model
• Many vision-language pretrained models

Late Fusion

Late fusion is the fusion at the decision-making level after each mode is completed with feature extraction and processing.

How it works：

• All or most of the processing is completed through independent networks
• The results of merging each modal are only at the final decision or output layer
• Usually, the results are integrated through voting, average or learning weights.

advantage：

• Simple implementation, each modal can be optimized independently
• Strongly robust to modal deletion
• Flexible model structure and easy to expand

shortcoming：

• Difficult to capture complex cross-modal interactions
• Complementary information between modals may be missed
• Overall performance may be limited by the performance of a single mode

Application Cases：

• Multimodal emotion analysis system
• Some multi-expert fusion models

Hybrid Fusion

Hybrid fusion combines the advantages of the above-mentioned fusion methods and performs multiple fusions at different levels.

How it works：

• Implement different types of convergence strategies at different levels of the network
• It may contain both early, mid and late fusion elements
• Control information flow through complex attention mechanisms or gated mechanisms

advantage：

• Ability to capture modal interactions at different levels at the same time
• Performance is usually better than a single fusion method
• More flexible information integration method

shortcoming：

• Complex structure and high calculation cost
• More parameters and more complex training processes are required
• It's difficult to tune

Application Cases：

• The latest multimodal large language models (such as GPT-4V, Gemini, etc.)
• High-performance multimodal understanding system

The choice of multimodal fusion depends on the specific application scenario, available resources and performance requirements. In practical applications, researchers and engineers need to select appropriate fusion strategies based on task characteristics and resource constraints, or design new fusion methods to meet specific needs.

Visual Encoder

A vision encoder is a key component in a multimodal large language model that is responsible for processing visual information. It converts visual data such as images or videos into feature representations that the model can handle. In multimodal large language models, vision encoders often employ pre-trained visual models to leverage their representational capabilities learned on large-scale visual data.

Mainstream visual encoder

CLIP ViT

CLIP ViT (Vision Transformer) is a visual encoder developed by OpenAI and is the visual part of the CLIP (Contrastive Language-Image Pre-training) model.

Features：

• Pre-training on 400 million images-text-to-data through comparative learning methods
• Ability to generate visual features that are aligned with text semantics
• Strong zero-sample migration capability
• Available in multiple sizes, from ViT-B/32 to ViT-L/14

application：

• It is widely used in multimodal large language models, such as LLaVA, GPT-4V, etc.
• Excellent performance in tasks such as image classification and image retrieval

DINOv2

DINOv2 is a self-supervised learning visual encoder developed by Meta AI.

Features：

• Training using self-distillation and self-supervised learning methods
• Ability to extract high-quality visual features, especially suitable for fine-grained visual understanding tasks
• Have strong semantic understanding of objects and scenes in images
• Learning visual representation without manual annotation

application：

• Use in multimodal models that require fine-grained visual understanding
• It is used in multimodal models such as SPHINX-X

SigLIP

SigLIP (Sigmoid Loss for Language Image Pre-training) is an improved vision-language pre-training model.

Features：

• Further optimization based on CLIP, using sigmoid loss function instead of the original comparison loss
• Provide better semantic alignment
• Training on large-scale data sets, with strong generalization ability
• Excellent performance in various visual-language tasks

application：

• It is used in multimodal models such as Cobra
• Excellent in applications requiring high-quality visual-language alignment

ConvNeXt

ConvNeXt is a visual encoder that combines the advantages of CNN and Transformer.

Features：

• The inductive bias of CNN is retained, and the design concept of Transformer is also borrowed from
• Provides efficient visual feature extraction capabilities
• A good balance between computing efficiency and performance
• Provides multiple scale versions to adapt to different resource constraints

application：

• It is used in multimodal models such as SPHINX-X
• Advantages in multimodal applications in resource-constrained environments

Multi-encoder collaboration

Some advanced multimodal models use multiple vision encoders to work together to obtain a more comprehensive visual representation.

BRAVE

The BRAVE model adopts a multi-encoder collaboration strategy:

How it works：

• Connect features of multiple different visual encoders in sequence
• Further refining and integrating features through MEQ-Former
• Using the complementary advantages of different encoders to improve visual understanding

Cobra

The Cobra model integrates a variety of visual encoders:

How it works：

• Integrate DINOv2 and SigLIP as visual backbone
• Combining the low-level spatial features of DINOv2 and the semantic properties provided by SigLIP
• Integrate the outputs of different encoders through a specially designed fusion mechanism

SPHINX-X

SPHINX-X adopts a dual encoder strategy:

How it works：

• Using two visual encoders DINOv2 and CLIP-ConvNeXt
• Provide complementary visual representations through different learning methods and network architectures
• Advantages of designing a specialized fusion mechanism to integrate two encoders

Lightweight visual encoder

To deploy multimodal models in resource-constrained environments, researchers have developed a lightweight vision encoder.

ViTamin

ViTamin is a lightweight visual model designed for resource-constrained environments.

Features：

• Visual encoding is completed through two layers of MBC (Multi-scale Block Convolution) and one layer of attention block
• The parameter volume is only 436M, which is much lower than that of traditional visual encoders
• Achieve 82.9% accuracy on ImageNet zero-shot, exceeding EVA-E with parameter volume of 4.4B
• Maintaining high performance while significantly reducing compute and storage requirements

application：

• Multimodal applications in mobile devices and edge computing environments
• Advantages in resource-constrained real-time systems

The choice of visual encoder has an important impact on the performance of multimodal large language models. Different visual encoders have different characteristics and advantages and are suitable for different application scenarios. In practical applications, it is necessary to select the appropriate visual encoder according to task requirements, computing resources and performance requirements, or adopt a multi-encoder collaboration strategy to obtain a more comprehensive visual representation.

Pre-training and fine-tuning methods

The training of multimodal large language models is usually divided into two stages: pre-training and fine-tuning. This paradigm enables the model to first learn general multimodal representations and then adapt to specific downstream tasks.

Pre-training method

Comparative learning pre-training

Comparative learning is one of the most commonly used methods in multimodal pre-training, which pushes away mismatched modal pairs by pulling closer to the representation of matching modal pairs (such as corresponding images and text).

How it works：

• Construct positive sample pairs (matched image-text pairs) and negative sample pairs (matched image-text pairs)
• Optimize the model using a contrast loss function (such as InfoNCE) so that the positive sample pairs are similar and the negative sample pairs are low
• Learn semantic alignment between modals through large-scale data training

Representative model：

• CLIP: Training on 400 million images-text pairs to learn powerful vision-language alignment representations
• ALIGN: Train data using larger scale noise images - text
• BLIP: A hybrid pre-training method combining contrast learning and generative learning

Mask pre-training

Mask pre-training learns representations within and between modals by predicting the masked input portion.

How it works：

• Randomly mask part of the input (such as image area or text token)
• Training the model to predict or reconstruct the masked part
• Can be applied to both single-modal and cross-modal prediction tasks simultaneously

Representative model：

• BEiT-3: Unified mask-automatic pre-training framework, processing images, text and image-text pairs simultaneously
• SimVLM: Visual-language pretraining using prefix language modeling
• OFA: Unified sequence-to-sequence pre-training framework, supporting multiple mask prediction tasks

Generative pre-training

Generative pre-training learns mapping relationships between modals by generating contents of one modality based on another modality.

How it works：

• Given an input of one modal (such as an image), generate an output of another modal (such as a description text)
• Optimize the model using generative losses (such as cross entropy)
• Through large-scale data training, learn the ability to convert between modals

Representative model：

• DALL-E: Generative pre-trained model for generating images from text
• CoCa: Dual-objective pre-training combining contrast learning and generative learning
• Flamingo: Process interlaced visual and language input through generative pre-training learning

Fine-tuning method

Instruction fine-tuning

Instruction fine-tuning is the ability to adapt pretrained models to follow natural language instructions.

How it works：

• Build a dataset containing various instructions and corresponding responses
• Use this data to fine-tune the pretrained model so that it can understand and execute instructions
• Usually supervised training

Representative Method：

• InstructBLIP: Fine-tuning instructions based on BLIP-2 to improve multimodal instruction compliance capabilities
• LLaVA: Use multimodal instruction data generated by GPT-4 for fine-tuning
• MiniGPT-4: Fine-tuning instructions through two-stage alignment strategy

Alignment fine adjustment

Alignment fine-tuning is designed to align the output of the model with human preferences and values.

How it works：

• Collect human feedback data, including preference labeling or sorting
• Optimize the model using reinforcement learning or other methods to make its output more in line with human preferences
• Usually trained in combination with safety and usefulness considerations

Representative Method：

• RLHF (reinforcement learning based on human feedback): training reward models using human preference data, and then optimizing strategies with reinforcement learning
• DPO (Direct Preference Optimization): Learn directly from human preference data to avoid explicit reward modeling
• Constitutional AI: Use a set of principles to guide model generation and self-criticism

Low resource fine-tuning

The low resource fine-tuning method is designed to effectively adapt to pretrained models using limited computing resources and data.

How it works：

• Only a small part of the model's parameters are updated, keeping most parameters frozen
• Use high-efficiency fine-tuning techniques such as adapters, LoRA, etc.
• Reduce calculation requirements through knowledge distillation or other techniques

Representative Method：

• LoRA (low rank adaptation): Update the weight by low rank decomposition matrix, greatly reducing trainable parameters
• Adapter: Insert small trainable modules between Transformer layers to keep the original model parameters unchanged
• QLoRA: Combining quantization and LoRA to further reduce memory requirements

Datasets and training strategies

Multimodal pre-training dataset

• LAION-5B: Large-scale dataset containing 5.8 billion image-text pairs, widely used in pre-training of multimodal models
• CC12M: Dataset containing 12 million images-text pairs, with high quality
• COYO-700M: Contains 700 million high-quality, diverse images-text pairs
• MMC4: Multimodal web page data extracted from Common Crawl, containing image, text and layout information

Training strategies

• Course study: From simple to complex, gradually train the model to improve learning efficiency and performance
• Multitasking learning: Optimize multiple related tasks at the same time to improve the generalization ability of the model
• Continuous pre-training: Continue to pre-train existing models on new data to adapt to new fields or tasks
• Mixed precision training: Use different numerical accuracy to balance calculation efficiency and model performance

The selection of pre-training and fine-tuning methods has an important influence on the performance and applicability of multimodal large language models. Different methods are suitable for different application scenarios and resource constraints. In practical applications, it is necessary to select appropriate training strategies based on specific needs and available resources, or combine multiple methods to achieve the best results.

Cross-modal alignment technology

Cross-modal alignment is one of the core challenges of multimodal large language models, which aims to establish semantic connections between different modalities, allowing the model to understand and generate cross-modal content. This section will introduce the main cross-modal alignment technologies and their applications.

Indicates alignment

Representation alignment aims to map features of different modalities to a shared semantic space so that semantic similar contents are closer to that space.

Comparative learning alignment

How it works：

• Optimize the model using a contrast loss function so that the matching modal pairs (such as the corresponding images and text) are close to the feature space
• At the same time, push the mismatched mode pairs to increase their distance in the feature space
• Usually used to implement loss functions such as InfoNCE and NT-Xent

advantage：

• Ability to learn powerful cross-modal representations
• Suitable for zero-sample transfer learning
• Stable training, good results

Application Cases：

• CLIP: Use contrast to learn to align images and text representations
• ALIGN: Applying contrast learning on larger and noisier data
• ALBEF: Alignment in combination with contrast learning and mask language modeling

Shared space mapping

How it works：

• Design a special mapping network to project features of different modalities into a shared semantic space
• Apply various constraints and loss functions in shared space to ensure semantic consistency
• It can be implemented using autoencoder, variational autoencoder and other technologies

advantage：

• Provide more flexible mapping methods
• Can handle structural differences between modals
• Supports multimodal fusion and generation

Application Cases：

• FLAVA: Using a combination of shared encoder and modal specific encoder
• BEiT-3: Unified mask-automatic framework, learning shared multimodal representation
• CoCa: Learning shared representations through comparison and generation of goals

Attention Alignment

Attention Alignment uses attention mechanisms to establish fine-grained correspondence between different modal elements.

Cross attention

How it works：

• Use features of one modal as query and features of the other as keys and values
• Calculate the similarity between the query and the key and generate attention weights
• Generate context representation based on the attention weighted value vector

advantage：

• Able to capture fine-grained modal correspondence
• Provides interpretable alignment results
• Suitable for processing structured and unstructured data

Application Cases：

• ViLBERT: Connecting Vision and Language Transformer with Cross Attention
• LXMERT: Designing visual-language cross-attention layers for modal fusion
• Flamingo: Inserting cross attention layers in language model to process visual information

Self-attention fusion

How it works：

• Splicing or interlacing features of different modes
• Use self-attention mechanism to deal with mixed feature sequences
• Learn the relationship between modals through interaction of the self-attention layer

advantage：

• Simple implementation and easy integration into existing models
• Allow global interaction between all modal elements
• Suitable for handling mixed inputs of multiple modalities

Application Cases：

• VisualBERT: Apply self-attention after splicing visual and language features
• ALBEF: Multimodal representation of fusion using self-attention processing
• OFA: Unified sequence-to-sequence framework, using self-attention to process multimodal inputs

Semantic Alignment

Semantic alignment focuses on high-level semantic relationships between different modalities, ensuring that the model can understand the concepts and knowledge of cross-modality.

Pre-training task design

How it works：

• Design specific pre-training tasks to facilitate semantic alignment between modals
• Including cross-modal matching, cross-modal generation, cross-modal inference and other tasks
• Optimize the semantic understanding ability of the model through multi-task learning

advantage：

• Directly optimize for semantic understanding
• Can design tasks in combination with domain knowledge
• Improve the generalization and migration capabilities of the model

Application Cases：

• UNITER: Use image-text matching, mask language/region modeling and other pre-training tasks
• OSCAR: Use object labels as anchors for cross-modal alignment
• SimVLM: Simple visual-language pretraining using prefix language modeling tasks

Knowledge-enhanced alignment

How it works：

• Introduce external knowledge bases or structured knowledge
• Use knowledge to guide the alignment process between modals
• Enhance semantic understanding through techniques such as knowledge distillation or knowledge graph

advantage：

• Provide richer semantic information
• Reduce data sparseness problems
• Improve the performance of the model in specific fields

Application Cases：

• ERNIE-ViL: Introducing structured knowledge to enhance vision-language pre-training
• K-LITE: Knowledge-enhanced lightweight image-text model
• KOSMOS-2: Language model with multimodal knowledge and tool usage capabilities

Assessment and Challenges

Alignment evaluation method

• Cross-modal search: Evaluate the performance of the model in the image-text retrieval task
• Zero sample classification: Test the ability of a model to migrate text knowledge to visual tasks
• Visual Q&A: Evaluate the model's ability to understand image content and answer questions
• Alignment visualization: Visualize the correspondence between modes through attention map or activation mapping

Alignment Challenge

• Modal Differences: Data of different modes have different statistical characteristics and structures
• Semantic Dividing: There are differences in the abstraction level and expression of cross-modal concepts
• Data quality: Noise and deviation in large-scale multimodal data affect alignment quality
• Computational efficiency: High-quality alignment often requires a lot of computing resources and complex models

Cross-modal alignment technology is a key component of multimodal large language models, which determines the model's ability to understand and generate cross-modal content. As the research deepens, more advanced alignment methods will continue to emerge, further improving the performance and application scope of multimodal large language models.

Multimodal representation learning

Multimodal representation learning is the basis of multimodal large language model, which focuses on how to learn to effectively capture feature representations of information in different modalities. This section will introduce the main methods and techniques of multimodal representation learning.

Jointly express learning

Joint representation learning aims to learn a unified feature that can represent multiple modal information simultaneously.

Shared embedded space

How it works：

• Map features of different modalities to a shared embedding space
• In a shared space, cross-modal content with similar semantics has similar representations
• Usually achieved through comparison learning, measurement learning and other methods

advantage：

• Easy to cross-modal retrieval and matching
• Support zero-sample transfer learning
• Compact expression, high computing efficiency

Application Cases：

• CLIP: Learning shared embedding space for images and text
• ALIGN: Learning shared representations on larger data
• FLAVA: Learning unified vision-language representation using shared encoder

Multimodal fusion representation

How it works：

• Integrate the features of different modalities through complex fusion mechanisms
• Learning can capture the representation of interaction and complementary information between modals
• Usually, it is achieved using attention mechanism, gate mechanism and other technologies.

advantage：

• Able to capture complex relationships between modals
• Keep modal-specific important information
• Suitable for complex multimodal understanding tasks

Application Cases：

• ViLBERT: Learning visual-language fusion representation using cross attention
• LXMERT: Designing a specialized cross-modal encoder learning fusion representation
• ALBEF: Learning multimodal representation through multi-stage fusion

Collaborative representation of learning

Collaborative representations learn to maintain independent representations of each modality while ensuring consistency and complementarity between them.

Alignment representation

How it works：

• Learn independent representations for each modal
• Ensure consistency between different modal representations through specific alignment constraints
• Alignment can be achieved using comparison losses, reconstruction losses, etc.

advantage：

• Preserve modal-specific information structures
• High flexibility, easy to expand to new modes
• Strongly robust to modal deletion

Application Cases：

• CLIP: Align independent visual and text representations through contrast
• ALIGN: Learning aligned representation on large-scale noise data
• BLIP: Combining contrast learning and generating learning to align vision-language representation

Complementary representation

How it works：

• Learning multimodal representations that complement each other
• Design specific learning objectives to facilitate different modal representations to capture complementary information
• Usually combined with information bottleneck theory, multi-view learning and other methods

advantage：

• Make full use of the complementarity of multimodal data
• Improve the amount of information and distinction of representations
• Suitable for handling modal incomplete or noise situations

Application Cases：

• CMC: Learn complementary representation using contrasting multi-view coding
• CLIP-ViP: Enhance the visual representation of CLIP through visual cues
• ALBEF: Optimizing complementary visual-language representation through multitasking learning

Hierarchical representation of learning

Hierarchical representations learn to focus on learning multimodal representations at different levels of abstraction, from low-level features to high-level semantics.

Multi-level fusion

How it works：

• Modal fusion at different levels of neural network
• Low-level fusion captures perceptual features, high-level fusion captures semantic concepts
• Multi-level information flow through technologies such as jump connection or feature pyramids

advantage：

• Ability to capture cross-modal relationships at different levels at the same time
• Provides richer representation capabilities
• Suitable for handling complex multimodal understanding tasks

Application Cases：

• ViLT: Visual-language fusion at all levels of Transformer
• UNITER: Learning hierarchical multimodal representation using multi-layer Transformer
• M-BERT: Fusion of multimodal information at different layers of BERT

Progressive learning

How it works：

• Start with a simple representation learning task and gradually transition to complex tasks
• First learn the modal representation, then learn the cross-modal representation
• Through course learning or multi-stage training

advantage：

• Improve learning efficiency and stability
• Reduce catastrophic forgetting problems
• Suitable for processing complex multimodal data

Application Cases：

• ALBEF: Adopting multi-stage pre-training strategy
• BLIP-2: Gradually bridge visual and language models through Q-Former
• LLaVA: First learn visual-language alignment, then perform instruction fine-tuning

Self-supervision means learning

Self-supervision means learning to design pre-training tasks using the data itself, without the need for a large amount of manual annotation.

Mask reconstruction

How it works：

• Randomly mask part of the input (such as image area or text token)
• Training the model to predict or reconstruct the masked part
• Can be applied to single-modal or cross-modal scenarios

advantage：

• No manual data labeling is required
• Promote the model to learn deep semantic understanding
• Suitable for various modalities and tasks

Application Cases：

• BEiT-3: Unified Mask Self-Coding Pre-training Framework
• BERT: Learn text representation through mask language modeling
• MAE: Learn visual representation through mask self-encoding

Comparative learning

How it works：

• Construct positive sample pairs (sessions with similar semantics) and negative sample pairs (sessions with different semantics)
• Optimize the model to make the representations of positive sample pairs similar, and the representations of negative sample pairs different
• Can be applied within single mode or across modes

advantage：

• Learning a distinctive expression
• No precise labeling required
• Suitable for large-scale pre-training

Application Cases：

• CLIP: Learning through image-text comparison
• SimCLR: Construct positive sample pairs through data augmentation to perform visual representation learning
• ALBEF: Combining contrast learning and mask language modeling

Generative learning

How it works：

• Training the model to generate contents of one modal based on another modal
• Optimization model by reconstructing or generating loss
• It can be one-way generation or two-way generation

advantage：

• Promote deep semantic understanding between modals
• Learning Generative Ability and Understanding Ability
• Suitable for creative applications and content generation

Application Cases：

• DALL-E: Generate images from text
• CoCa: Combined with contrast learning and image description generation
• SimVLM: Visual-language pre-training through prefix language modeling

Multimodal representation learning is one of the core technologies of multimodal large language models, which determines the model's ability to understand and generate multimodal content. As the research deepens, more advanced representation learning methods will continue to emerge, further improving the performance and application scope of multimodal large language models.

Application of attention mechanism in multimodal

Attention mechanism is a key technology in multimodal large language model, which enables the model to selectively pay attention to important information in different modes and establish correlations between modes. This section will introduce the main forms of application of attention mechanisms in multimodal models.

Self-attention mechanism

The self-attention mechanism enables the model to capture long-distance dependencies within the sequence and is a core component of the Transformer architecture.

Single-modal self-attention

How it works：

• Calculate attention weights between each element and all elements in a sequence
• Weighted information based on attention weight
• Usually used to achieve the focus using the scaling dot component

Application in multimodal：

• Process sequences of different modalities, such as text token sequences or image patch sequences
• Capture the structure and relationships within the modal
• Provide rich feature representations for subsequent cross-modal fusion

Application Cases：

• ViT: Use self-attention to process image patch sequences
• BERT: Use self-attention to process text token sequences
• ViLT: Use self-attention to process visual and language features separately before fusion

Global self-attention

How it works：

• Splicing or interleaving features of different modalities into a unified sequence
• Use self-attention mechanism to process mixed sequences
• Allows direct interaction between different modal elements

advantage：

• Simple and direct, easy to implement
• Allow global interaction between all modal elements
• Suitable for handling mixed inputs of multiple modalities

Application Cases：

• VisualBERT: Apply self-attention after splicing visual and language features
• ALBEF: Multimodal representation of fusion using self-attention processing
• OFA: Unified sequence-to-sequence framework, using self-attention to process multimodal inputs

Cross Attention Mechanism

The cross-attention mechanism is specially designed to handle interactions between different modes and is the core technology of multimodal fusion.

One-way cross attention

How it works：

• Use features of one modal as query and features of the other as keys and values
• Calculate the similarity between the query and the key and generate attention weights
• Generate context representation based on the attention weighted value vector

advantage：

• Establish a clear mapping from one modal to another
• Suitable for processing conversion tasks from source to target mode
• High computing efficiency

Application Cases：

• Show, Attend and Tell: Use image features to guide text generation
• LXMERT: Use language features to query visual features
• Flamingo: Inserting cross attention layers in language model to process visual information

Two-way cross attention

How it works：

• Computing the cross attention from modal A to modal B and from modal B to modal A simultaneously
• Capture modal interactions in both directions
• Usually implemented through two independent cross attention modules

advantage：

• Capture more comprehensive intermodal relationships
• Suitable for tasks that require two-way understanding
• Provides richer fusion representations

Application Cases：

• ViLBERT: Connecting Visual and Language Transformer with Two-way Cross Attention
• LXMERT: Designing visual-language cross-attention layers for two-way interaction
• ALBEF: Enhanced multimodal alignment with bidirectional cross attention

Bulls' attention

Multiple attention is calculated in parallel through multiple attention "heads" to capture relationships and patterns in different aspects.

Self-attention of bulls

How it works：

• Project queries, keys, and values ​​to multiple subspaces
• Independent calculation of attention in each subspace
• Stitch and project the output of multiple headers back to the original dimension

Application in multimodal：

• Capture different types of intramodal relationships simultaneously
• Provide richer feature representations
• Enhance the expression ability of the model

Application Cases：

• ViT: Use multi-head self-attention to process image features
• BERT: Use multi-head self-attention to handle text features
• UNITER: Using multi-head self-attention in a unified multimodal Transformer

Cross-attention of bulls

How it works：

• Project features of different modes to multiple subspaces
• Compute cross attention independently in each subspace
• Stitch and project the output of multiple headers back to the original dimension

advantage：

• Capture the relationship between modals in different aspects
• Improve the expression and flexibility of the model
• Suitable for complex cross-modal understanding tasks

Application Cases：

• ViLBERT: Connect vision and language using multi-head cross attention
• LXMERT: Using multi-head cross attention in visual-language cross encoder
• Flamingo: Using multi-head cross attention to handle visual and verbal information

Advanced Attention Variants

To solve specific multimodal problems, researchers have developed a variety of advanced attention variants.

Layered attention

How it works：

• Applying attention mechanisms at different levels
• Low-level attention to deal with local characteristics, high-level attention to deal with global relationships
• Organize information flow through hierarchy

advantage：

• Ability to capture relationships of different granularities at the same time
• Improve computing efficiency
• Suitable for processing structured data

Application Cases：

• HAN: Use hierarchical attention to handle document structures
• LCGN: Visual reasoning using hierarchical map attention
• HiVLP: Hierarchical vision-language pretrained model

Sparse attention

How it works：

• Calculate only the attention between some pairs of elements, not all pairs of all
• Determine the attention calculation object through predefined patterns or dynamic selection
• Significantly reduce computational complexity

advantage：

• Significantly improve computing efficiency
• Suitable for processing long sequences
• Reduce memory requirements

Application Cases：

• Longformer: Use a combination of local windows and global attention
• BigBird: Combining random, window and global attention
• Perceiver: Map input to potential representation using cross attention

Perceptual resampler

How it works：

• Use a set of learnable latent vectors as query
• Extract information from original features through cross attention
• Convert variable-length inputs into fixed number of potential vectors

advantage：

• Significantly reduce sequence length and improve computational efficiency
• Suitable for processing high-dimensional inputs
• Convenient to fusion between different modes

Application Cases：

• Perceiver: Use a perceptual resampler to process multimodal inputs
• Flamingo: Use a perceptual resampler to process visual features
• Perceiver IO: General encoding-decoding architecture, suitable for multiple modalities

Attention mechanism is one of the core technologies of multimodal large language models, which enables the model to effectively process and fuse information from different modalities. As the research deepens, more advanced attention variants will continue to emerge, further improving the performance and application scope of multimodal large language models.

Application scenarios

Multimodal Large Language Models (MLLMs) are finding a wide range of applications in various industries with their strong cross-modal understanding and generation capabilities. This chapter will thoroughly explore the main application scenarios of multimodal large language models, from general applications to professional applications in vertical fields, and fully demonstrate the actual value and potential of this technology.

Content creation and generation application

The multimodal large language model demonstrates strong capabilities in the field of content creation, providing creators with new tools and possibilities.

Multimodal content generation

Technical Principles：

• Generate relevant images, videos, or audio content based on text prompts
• Generate matching text descriptions or stories based on visual input
• Create coherent multimodal content with multiple modal inputs

Main applications：

1. Text to image generation

   • Generate images that meet the requirements based on detailed text description
   • Supports stylized creations, such as imitating a specific artist's style or art genre
   • Application case: DALL-E 3 can generate high-quality images based on user's text descriptions, and Midjourney can create visual works with diverse artistic styles

2. Image assisted writing

   • Generate related articles, stories or descriptions based on images
   • Create copywriting for images that matches a specific style or purpose
   • Application case: GPT-4V can view images and create related stories or articles, Claude 3 can analyze images and generate detailed descriptions or content

3. Multimodal content enhancement

   • Add pictures or visual elements to existing content
   • Automatically generate titles, descriptions or labels based on images
   • Application case: Gemini can automatically generate relevant picture suggestions for blog posts, and Wen Xinyiyan can generate SEO-friendly descriptions for images

Creative design and artistic creation

Technical Principles：

• Using multimodal understanding capabilities to analyze design requirements and reference materials
• Create design works that meet specific styles and requirements through generative models
• Iterative optimization combined with user feedback

Main applications：

1. Concept design and prototype

   • Generate product concept diagram or design prototype based on text description
   • Quickly visualize creative ideas
   • Application case: Designers use DALL-E 3 to generate preliminary product design concepts and then perform professional optimization

2. Brand visual asset creation

   • Generate image and visual elements that match the brand tone
   • Create a consistent brand visual language
   • Application Case: Marketing Team Uses Midjourney to Generate Brand-Style Social Media Images

3. Art Exploration and Creation

   • Assist artists to explore new creative directions and styles
   • Generate creative inspiration and reference materials
   • Application Case: Artists use Stable Diffusion to explore different art styles and creative possibilities

Content localization and adaptation

Technical Principles：

• Understand the semantics and cultural context of the original content
• Generate equivalents that adapt to the target language and culture
• Maintain the core information and emotional tone of the content

Main applications：

1. Multilingual content creation

   • Translate and adapt content to different languages ​​and cultural backgrounds
   • Generate text that conforms to local language habits
   • Application case: Global enterprises use GPT-4o to translate marketing materials and adapt to different markets

2. Cross-cultural visual adaptation

   • Adjust visual content to conform to the aesthetics and taboos of different cultures
   • Generate alternative images for specific cultural contexts
   • Application case: Advertising companies use multimodal models to adjust advertising visual elements to suit different regional markets

3. Multimodal content reconstruction

   • Reorganize and present content according to the preferences of the target audience
   • Adjust the complexity and professionalism of the content
   • Application case: Educational institutions use Claude 3 to reconstruct professional content into a form suitable for learners of different ages

Multimodal dialogue system application

The multimodal dialogue system integrates multiple modes such as text, images, audio, etc. into dialogue interaction to create a more natural and richer human-computer interaction experience.

Visually enhanced dialogue

Technical Principles：

• Integrate visual input into dialogue system
• Understand image content and quote relevant information in conversation
• Generate a response that takes into account the visual context

Main applications：

1. Visual Q&A Assistant

   • Answer questions about user-provided images
   • Explain the content, relationships and details in the image
   • Application case: Users show a photo to GPT-4V and ask about landmarks or objects. The system can identify and provide relevant information

2. Visually guided dialogue

   • Conversation based on shared visual content
   • Discuss elements in images and provide relevant suggestions
   • Application case: User discusses a home decoration photo with Claude 3 to obtain design suggestions and improvement opinions

3. Multiple rounds of visual interaction

   • Maintain visual context during multiple rounds of conversation
   • Allow users to gradually explore and understand visual content through dialogue
   • Application case: Users have multiple rounds of conversations with Gemini to gradually analyze and discuss a complex chart or design

Multimodal virtual assistant

Technical Principles：

• Integrate multiple modal input and output capabilities
• Maintain cross-modal dialogue context
• Choose the most appropriate response mode according to user needs

Main applications：

1. Personal life assistant

   • Help users handle daily tasks such as identifying items and interpreting documents
   • Provide personalized suggestions based on visual input
   • Application case: Users show the ingredients in the refrigerator to GPT-4o and obtain feasible recipe suggestions

2. Work efficiency assistant

   • Assist in analyzing work documents, charts and presentations
   • Provide professional advice based on visual content
   • Application Case: Professionals use Claude 3 to analyze business reports and data visualizations to gain insights and suggestions

3. Study Tutoring Assistant

   • Answer students' questions about textbooks, homework or charts
   • Provide visual explanations and teaching content
   • Application case: Students use Wen Xin Yiyan to understand complex scientific charts or mathematical problems

Situational Perception Interaction

Technical Principles：

• Understand the physical environment and context of the user
• Integrate real-time visual information into conversation
• Provide responses and suggestions related to the current situation

Main applications：

1. Real-time environment understanding

   • Analyze the user's surroundings and provide relevant information
   • Identify objects, texts, and scenes in the environment
   • Application case: Users use Gemini to identify buildings or artworks during travel to obtain relevant historical and cultural information

2. Situation-related suggestions

   • Provide suggestions based on the visual environment that suits the current situation
   • Generate responses considering time, place, and visual clues
   • Application case: Users use GPT-4V to analyze products in the store to obtain comparisons and recommendations

3. Augmented reality dialogue

   • Overlay virtual information into the vision of the real environment
   • Interact with augmented reality content through dialogue
   • Application case: Users interact with multimodal assistant through AR glasses to obtain real-time information and guidance on the objects they see

Visual Q&A and Understanding Application

Visual Q&A (VQA) is one of the core applications of multimodal large language models, which allows users to ask questions about images and obtain answers based on image content.

Universal visual question and answer

Technical Principles：

• Handle image input and text problems simultaneously
• Analyze the image content to find visual information related to the problem
• Generate text answers based on visual comprehension

Main applications：

1. Object recognition and description

   • Identify objects, characters, or scenes in images
   • Describe the properties, states, and relationships of an object
   • Application case: The user uploads a photo and asks "What kind of flower is this?" The model can identify and provide the name and information of the flower

2. Scene understanding and explanation

   • Understand the overall scenes and activities in the image
   • Explain events and contexts in a scene
   • Application case: The user shares a street scene photo and asks "What's going on here?" The model can describe the activities and situations in the scene

3. Visual reasoning and judgment

   • Logical reasoning based on image content
   • Answer questions that require visual judgment
   • Application case: The user presents a chessboard picture and asks "What is the best way to move next?" The model can analyze the chess game and provide suggestions

Visual understanding of professional fields

Technical Principles：

• Applying domain-specific knowledge understanding professional images
• Identify key elements and patterns in professional images
• Provide explanations and analysis in a professional context

Main applications：

1. Interpretation of medical imaging

   • Assist in the analysis of medical images such as X-ray, CT, MRI, etc.
   • Identify potential anomalies or areas of concern
   • Application case: Doctors use multimodal models to initially screen X-rays to mark areas that need attention

2. Scientific chart analysis

   • Understand and interpret charts and visualizations in scientific papers
   • Extract data and trends from charts
   • Application Case: Researchers use Claude 3 to analyze complex scientific charts, extracting key data points and trends

3. Engineering drawing understanding

   • Analyze engineering drawings and technical diagrams
   • Identify component and structural relationships
   • Application case: Engineers use GPT-4V to understand complex technical drawings, obtain component information and design details

Visual understanding of documents

Technical Principles：

• Combining OCR and semantic understanding capabilities
• Analyze the visual layout and structure of a document
• Extract and understand text and graphic content in a document

Main applications：

1. Table data extraction

   • Extract structured data from table images
   • Understand the row relationship and data meaning of tables
   • Application case: User uploads pictures of financial statements, and the model can extract key financial data and analyze it

2. Complex document understanding

   • Analyze complex documents containing text, charts, and images
   • Understand the relationship between parts of the document
   • Application case: Legal professionals use multimodal models to analyze contract documents, extract key terms and obligations

3. Understanding the content of mixed pictures and texts

   • Understand the relationship between text and pictures
   • Integrate graphic information to provide a comprehensive understanding
   • Application case: Students use Gemini to understand the mixed text and pictures in textbooks and obtain complete explanations of knowledge points

Cross-modal search and search applications

Cross-modal retrieval allows users to use queries of one modal (such as text) to retrieve the contents of another modal (such as images), greatly expanding the way and scope of information acquisition.

Text to image retrieval

Technical Principles：

• Map text queries to visual feature space
• Calculate the similarity between the query and all images in the image library
• Return the most similar image results

Main applications：

1. Image search based on description

   • Search for matching images using natural language description
   • Supports abstract concepts and complex scene descriptions
   • Application case: Designers use text to describe "city skyline at sunset" to search for related image materials

2. Visual creative exploration

   • Explore visual creativity with conceptual description
   • Discover relevant visual content based on text prompts
   • Application case: Creative Director uses abstract concepts such as "Future of Futurism and Nature" to search for inspiration images

3. Multi-attribute image query

   • Combining multiple attributes and conditions for accurate image search
   • Supports complex query logic and filtering conditions
   • Application case: E-commerce platforms allow users to search product images using detailed text descriptions, such as "red leather flip ladies handbag"

Image to text retrieval

Technical Principles：

• Map images to text feature space
• Calculate the similarity between images and all documents in the text library
• Return the most relevant text content

Main applications：

1. Visual content matching

   • Use images to find relevant articles, reports, or descriptions
   • Recommend related reading materials based on image content
   • Application case: User uploads architectural photos, and the system returns articles about the architectural style, history and characteristics.

2. Product information retrieval

   • Find detailed specifications and comments through product images
   • Identify products and match related documents
   • Application case: Consumers take product photos, obtain detailed specifications, user reviews and usage guides

3. Visual problem matching

   • Match image questions to related answers or tutorials
   • Find solutions based on visual content
   • Application case: Students take math problems and systematically match the problem-solving steps and explanations of similar problems

Multimodal content organization

Technical Principles：

• Create a unified representation for multimodal content
• Organize and cluster content based on semantic similarity
• Supports cross-modal content discovery and association

Main applications：

1. Intelligent media library management

   • Automatically classify and mark images, videos, and documents
   • Create an intelligent content-based organizational structure
   • Application case: Photographers use multimodal systems to automatically organize and mark large numbers of photos for easier subsequent retrieval

2. Knowledge graph construction

   • Extract entities and relationships from multimodal content
   • Building a knowledge graph that connects text and visual information
   • Application case: Research institutions use multimodal models to build scientific knowledge graphs from papers and graphs

3. Personalized content recommendations

   • Recommended content based on user-based multimodal interaction history
   • Personalized recommendations considering text and visual preferences
   • Application case: The content platform analyzes the image and text content browsed by users and provides personalized multimodal content recommendations

Vertical field applications

The multimodal large language model has shown great application potential in various vertical fields, from medical and health to education and training, from autonomous driving to cultural heritage protection, and is creating new values ​​and possibilities.

Medical and health field

Main applications：

1. Medical imaging-assisted diagnosis

   • Analyze radiographic images such as X-ray, CT and MRI to mark potential abnormal areas
   • Assist in the analysis of pathological sections to identify cell abnormalities and tissue changes
   • Generate preliminary medical imaging reports to improve diagnostic efficiency

2. Multimodal medical data integration

   • Comprehensive analysis of the patient's image, test report and medical history
   • Provide treatment advice and decision support based on multimodal medical data
   • Track the changing trends of patient health data and warn of potential risks

3. Medical Education and Training

   • Provide multimodal analysis and learning of real medical cases
   • Analyze the surgical video and provide step instructions and technical guidance
   • Create interactive medical knowledge Q&A and learning systems

Education and training field

Main applications：

1. Intelligent teaching assistant

   • Analyze student assignments and provide detailed feedback and suggestions for improvement
   • Transform abstract concepts into visual representations, providing intuitive explanations
   • Supports interactive Q&A to meet the needs of different learning styles

2. Educational content creation

   • Generate structured teaching materials containing text and images
   • Create interactive learning resources and visualization exercises
   • Visual auxiliary tools for teaching content development

3. Language Learning and Cultural Education

   • Related language concepts with visual representations and provide contextual learning
   • Explain the cultural elements and background knowledge related to language
   • Create a language conversation exercise based on real scenes

Autonomous driving and robotics

Main applications：

1. Scenario understanding and decision-making

   • Analyze complex traffic scenarios and road environments
   • Identify abnormal or dangerous situations to improve safety
   • Awareness of the environment in different weather and light conditions

2. Multimodal human-computer interaction

   • Understand the driver or user's voice commands and gestures
   • Provide information and services based on the current situation
   • Create a natural and intuitive interactive experience

3. Visual Navigation and Operation

   • Understand and execute natural language navigation instructions
   • Building semantic maps and spatial relationships of environments
   • Supports precise vision-based operations and task execution

Emerging application fields

Main applications：

1. Augmented reality and virtual reality

   • Overlay relevant information and interactive content for the real environment
   • Generate virtual environments and scenes based on text description
   • Create multimodal immersive learning and experience

2. Smart retail and shopping experience

   • Provide visual shopping assistant and product recognition services
   • Create virtual trials and product presentation experiences
   • Provide personalized shopping suggestions based on user needs and preferences

3. Cultural Heritage Protection and Dissemination

   • Analyze artifact images and provide detailed explanations and backgrounds
   • Create multimodal cultural stories and presentations
   • Promote cross-cultural understanding and knowledge dissemination

4. Environmental monitoring and protection

   • Identify and analyze wildlife images
   • Comparison of environmental changes in different periods
   • Identify visual evidence of environmental pollution and generate analysis reports

The application scenarios of multimodal large language models are constantly expanding. With the advancement of technology and innovative application design, we will see more amazing applications appearing in various fields. These applications not only improve efficiency and convenience, but also create new ways of interaction and service, which has a profound impact on human society.

Challenges and limitations

Despite the impressive progress made by multimodal large language models (MLLMs) in recent years, they still face a series of major challenges and limitations. These challenges involve technology, ethics, society and regulation, and profoundly affect the development and application of this technology. This chapter will explore in-depth the main challenges and limitations faced by multimodal large language models and possible solutions.

Technical Challenges

Modal alignment and fusion problems

One of the core challenges of multimodal large language models is how to effectively align and fuse information from different modes. Data of different modalities have different structural, dimensions and semantic properties, making their alignment and fusion particularly complex.

Key Challenges：

1. Semantic Dividing

   • There are essential semantic differences between different modalities
   • Visual information is usually continuous and high-dimensional, while text information is discrete and symbolized
   • It is difficult to establish accurate semantic mapping relationships between different modalities

2. Indicates that space is inconsistent

   • The features of different modalities are distributed in different representation spaces
   • Special mapping mechanisms are needed to project them into shared spaces
   • Achieve effective alignment while maintaining the integrity of information in each modal

3. Difficulty in cross-modal reasoning

   • Models require complex inferences between different modalities
   • Understand the causal relationship and logical connection between modals
   • Make reasonable inferences when a certain modal information is missing

Current solutions and limitations：

1. Comparative learning methods

   • Establish correlations between different modalities through comparative learning
   • Limitations: It may only learn shallow correlations, and it is difficult to capture deep semantics

2. Attention mechanism

   • Use mechanisms such as cross attention to achieve information exchange between modals
   • Limitations: High computational complexity, difficult to process long sequences or high resolution inputs

3. Pre-training-fine-tuning paradigm

   • Learn general representation through large-scale pre-training, and then fine-tune for specific tasks
   • Limitations: Pretrained data quality and diversity limit the generalization capability of the model

Computational resources and efficiency issues

Multimodal large language models usually have huge parameters and complex architectures, resulting in the training and inference process that consume a lot of computing resources.

Key Challenges：

1. High training cost

   • Training large-scale multimodal models requires a large number of GPU/TPU resources
   • Long training time and high energy consumption
   • Limits the participation of research institutions and enterprises

2. Delay problem of reasoning

   • Inference delay challenge in real-time applications
   • Calculate heavy burden when processing high-resolution images or long video sequences
   • Deployment difficulties in mobile devices and edge computing environments

3. Huge memory requirements

   • Model parameters and intermediate activation values ​​occupy a lot of memory
   • Memory consumption increases dramatically when processing high-resolution images
   • Limit batch size and processable input size

Current solutions and limitations：

1. Model compression technology

   • Compression methods such as quantization, pruning, knowledge distillation, etc.
   • Limitations: Compression often leads to performance degradation, especially on complex tasks

2. Efficient architecture design

   • Design a model architecture with higher computing efficiency
   • Limitations: There is a trade-off between efficiency and performance, and efficient architectures may sacrifice expressive capabilities

3. Distributed training and reasoning

   • Improve efficiency by using multi-device parallel processing
   • Limitations: Increase system complexity, communication overhead may become a new bottleneck

Data quality and diversity challenges

The performance of multimodal large language models depends to a large extent on the quality and diversity of training data. However, acquiring high-quality, diverse multimodal datasets remains a major challenge.

Key Challenges：

1. Data quality issues

   • Data crawled on the network usually contains noise, errors, and inaccurate information.
   • Image-text pairs are unevenly correlated and accurate
   • High cost of data cleaning and screening

2. Insufficient data diversity

   • Existing data sets are inadequate in terms of language, culture, and fields.
   • Causes the model to perform poorly in a specific population or field
   • Concepts and scenarios that are difficult to cover long-tail distribution

3. High labeling cost

   • High-quality multimodal data annotation requires expertise and a lot of manpower
   • The labeling of certain professional fields (such as medical care, law) is particularly difficult
   • Automatic labeling method may introduce systematic deviations

Current solutions and limitations：

1. Self-supervised learning method

   • Use the data intrinsic structure for self-supervised learning to reduce dependence on labeling
   • Limitations: Possible to learn surface correlations rather than deep semantics

2. Data enhancement technology

   • Expand existing data through transformation and synthesis
   • Limitations: Manually generated data may lack real-world complexity

3. Crowdsourcing and active learning

   • Use crowdsourcing platforms to collect annotations and use active learning strategies to improve efficiency
   • Limitations: Difficult in quality control, high cost of obtaining knowledge in professional fields

Robustness and generalization capability limitations

Multimodal large language models often show insufficient robustness when facing out-of-distribution data, adversarial samples, or incomplete inputs.

Key Challenges：

1. Distribution offset sensitivity

   • The model is highly sensitive to the offset between the training distribution and the test distribution
   • Performance may drop significantly in new areas or scenarios
   • Difficult to adapt to the diversity and changes in the real world

2. Fight against attack vulnerability

   • Vulnerable to confrontational attacks against visual or text input
   • Small, human-imperceptible perturbations may cause significant changes in model output
   • Constituting a significant risk in safety-critical applications

3. Poor adaptability of modal missing

   • Poor performance when information is missing or of low quality in a certain mode
   • Difficult to make reasonable inferences based on available information
   • Lack of effective uncertainty estimation mechanism

Current solutions and limitations：

1. Confrontational training

   • Introducing adversarial samples to enhance robustness during training
   • Limitations: High computational cost, which may affect performance on standard samples

2. Data Enhancement and Domain Adaptation

   • Improve generalization capabilities through diversified data augmentation and domain adaptation techniques
   • Limitations: Difficult to cover all possible distribution changes

3. Uncertainty Modeling

   • Introducing uncertainty estimation to enable the model to express the credibility of predictions
   • Limitations: Accurate uncertainty estimates are a challenge in themselves

Ethical and social issues

The development and application of multimodal large language models has raised a series of ethical and social issues that may have profound impacts on individuals and society.

Issues of prejudice and fairness

Multimodal large language models may inherit and amplify social biases in training data, leading to unfair results and decision-making.

Key Challenges：

1. Data bias transmission

   • Social biases in training data are learned and amplified by the model
   • Stereotypes in visual data (such as occupations, gender roles, etc.) are reinforced
   • Different population groups represent unevenly in the data

2. Multimodal bias amplification

   • Bias in different modalities may reinforce each other
   • The combination of bias in text and images creates stronger stereotypes
   • Difficult to identify and mitigate implicit biases across modalities

3. Incomplete evaluation criteria

   • Lack of standards and methods for comprehensively evaluating the fairness of multimodal models
   • Existing assessments tend to focus only on single-dimensional bias
   • Difficult to balance the needs of different groups and stakeholders

Current solutions and limitations：

1. Data intervention method

   • Representation of different groups in balanced training data
   • Limitations: Completely eliminating data bias is nearly impossible and new biases may be introduced

2. Algorithm fairness technology

   • Incorporate fairness constraints into training objectives
   • Limitations: There may be conflicts between different fairness indicators and it is difficult to meet at the same time.

3. Post-processing and manual review

   • Post-processing or manually auditing of model outputs to reduce bias
   • Limitations: High cost, difficult to apply on a large scale, and manual review may also be biased

Privacy and security risks

Multimodal large language models can involve privacy and security risks when processing and generating content, especially when they process sensitive information or are used to generate potentially harmful content.

Key Challenges：

1. Privacy data breach

   • The model may remember and leak personal privacy information from the training data
   • Visual data may contain more difficult-to-identify privacy elements
   • Sensitive information may be reconstructed or inferred through model output

2. Generate harmful content

   • Possible for misuse to generate false information, in-depth forgery or harmful content
   • Multimodal generation capability enhances the authenticity and persuasion of content
   • Difficult to strike a balance between maintaining model capabilities and preventing abuse

3. Security vulnerability exploit

   • May be used for automated cyber attacks or social engineering attacks
   • Bypassing safety measures by prompt injection, etc.
   • Multimodal input increases attack surface and complexity

Current solutions and limitations：

1. Differential Privacy

   • Apply differential privacy technology to protect personal data during training
   • Limitations: It may reduce model performance and difficult to select parameters

2. Content filtering and secure alignment

   • Reduce harmful output using filters and safe alignment techniques
   • Limitations: It may be overly restricted in legitimate content, and attackers continue to discover new ways to bypass

3. Red Team Testing and Vulnerability Fix

   • Actively find and fix security vulnerabilities in models
   • Limitations: All possible attack methods cannot be foreseen, security and attack are a continuous arms race

Social impact and ethical considerations

The widespread application of multimodal large language models may have profound impacts on social structure, employment market and human cognition, causing a series of ethical issues.

Key Challenges：

1. Job market changes

   • Possibly automate certain tasks that rely on visual and language processing
   • Creative industry and knowledge workers face new challenges and opportunities
   • Skills demand and labor market structure may change

2. Impact of information ecosystem

   • May change the way content is created, communicated and consumed
   • The boundaries between real and generated content become blurred
   • Information credibility assessment becomes more difficult

3. Cognitive and social interaction changes

   • Possibly change the way humans acquire knowledge and understand the world
   • Influence interpersonal communication and social interaction patterns
   • May lead to excessive dependence or trust in AI systems

Current solutions and limitations：

1. Responsible AI development framework

   • Guidelines for establishing ethical principles and best practices
   • Limitations: Difficulties in execution and supervision, differences between cultures and values

2. Multi-stakeholder participation

   • Engage diversified stakeholders in technology development and policy development
   • Limitations: Coordinate the complexity of different interests and perspectives, and the decision-making process may be slow

3. Education and awareness enhancement

   • Raise public awareness of AI capabilities and limitations
   • Limitations: Information asymmetry and technical complexity make comprehensive understanding difficult

Regulatory and legal challenges

With the rapid development and wide application of multimodal large language models, relevant regulatory and legal frameworks are being formed, but they still face many challenges.

Intellectual Property Issues

The training and generation of multimodal large language models involve complex intellectual property issues and challenges existing legal frameworks.

Key Challenges：

1. Copyright disputes on training data

   • Legality Issues for Training with Copyrighted Images and Text
   • The applicability of the "fair use" principle in AI training is unclear
   • Differences in legal provisions in different countries and regions

2. Attribution of generated content

   • The copyright ownership of AI-generated content is unclear
   • Difficult to define the contribution boundaries between human creators and AI systems
   • The existing intellectual property legal framework is difficult to adapt to the new paradigm of AI creation

3. Infringement risk management

   • The model may generate content that infringes on other people's intellectual property rights
   • Difficult to track and control all copyright elements in training data
   • Responsibility assignment issues: The boundaries of responsibility between developers, deployers, and users

Current solutions and limitations：

1. Licensing and authorization mechanisms

   • Establish a license agreement with the content owner
   • Limitations: It is difficult to cover massive data, and the transaction costs are high

2. Content filtering and detection

   • Development tools to detect and prevent infringement content
   • Limitations: It is technically difficult and cannot fully accurately identify all infringements

3. Legal framework update

   • Updated intellectual property laws to adapt to the AI ​​era
   • Limitations: The legislative process is slow and it is difficult to keep up with the speed of technological development

Responsibility and Accountability Mechanism

Determining the attribution of the responsibility for the negative consequences of multimodal large language models is a complex issue involving multiple stakeholders.

Key Challenges：

1. Unclear allocation of responsibilities

   • Blurred boundaries of responsibility between model developers, deployers and users
   • The behavior of autonomous systems may be difficult to predict and explain
   • Existing legal frameworks are difficult to cope with the complexity of AI systems

2. Insufficient transparency and interpretability

   • The decision-making process of multimodal models is usually opaque
   • Difficult to explain why the model generates a specific output
   • Lack of effective audit and accountability mechanisms

3. Cross-border liability issues

   • The global nature of AI systems makes cross-border responsibility more complex
   • Inconsistent laws and standards in different jurisdictions
   • Improper international coordination and cooperation mechanism

Current solutions and limitations：

1. Algorithm impact assessment

   • Assess the possible impact and risks of the system before deployment
   • Limitations: It is difficult to foresee all possible impacts, and the evaluation criteria are inconsistent

2. Interpretability technology

   • Develop technologies to improve transparency and interpretability of models
   • Limitations: Explanation is often simplified and may not fully reflect the decision-making process of complex models

3. Industry self-discipline and standards

   • Establish industry best practices and self-discipline mechanisms
   • Limitations: Lack of enforcement and may not effectively restrain all participants

Data privacy and security issues

Data processed by multimodal large language models usually contain sensitive information, and data privacy and security issues become important challenges.

Key Challenges：

1. Complexity of informed consent

   • Users have difficulty fully understanding how data is used and potentially impacted
   • Multimodal data (especially images) may contain unexpected personal information
   • Traditional consent mechanisms are difficult to adapt to the scale and complexity of AI training

2. Third-party data issues

   • Images and videos may contain third-party individuals who have not given consent
   • Difficult to identify and remove all unconsensual personal data from large-scale datasets
   • Consent management in data collected in public places is particularly complex

3. Data security risks

   • Large-scale data sets become targets for high-value attacks
   • Multimodal data breaches could lead to more serious privacy violations
   • Adversarial attacks may exploit the complexity of multimodal inputs

Current solutions and limitations：

1. Privacy protection technology

   • Differential privacy, federated learning and other technologies protect data privacy
   • Limitations: May affect model performance and complex implementation

2. Data Minimization Principle

   • Collect and use only the necessary data
   • Limitations: Possible limits on model functionality and performance

3. Privacy-Use Balance Mechanism

   • Dynamically manage the balance between privacy protection and model performance
   • Limitations: Difficult to quantify and optimize this balance

The challenges and limitations faced by multimodal large language models are multifaceted, involving multiple dimensions such as technology, ethics, society and supervision. These challenges not only affect the performance and scope of application of the model, but also affect the social acceptance and sustainability of technological development. Addressing these challenges requires technological innovation, policy development and the joint efforts of multistakeholders to ensure that the development of multimodal large language models can not only promote technological progress but also protect individual rights and social values.

Future trends

With the rapid development of multimodal large language model (MLLMs) technology, its future development trend has attracted much attention. This chapter will in-depth discussion on the future development direction, potential breakthrough points and possible application prospects of multimodal large language models, providing a forward-looking perspective for understanding the long-term evolution of this technology.

Technology development direction

Model architecture and scale evolution

The architecture and scale of the multimodal large language model will continue to evolve and develop in a more efficient and powerful direction.

Main trends：

1. Larger multimodal model

   • The scale of parameters continues to grow, moving from hundreds of billions to trillions of parameters
   • The scale and diversity of training data have been greatly improved
   • Breakthroughs in computing efficiency make larger-scale models possible

   This trend will bring a qualitative leap in model understanding and generation capabilities, allowing models to handle more complex multimodal tasks and demonstrate understanding that is closer to humans. However, this also presents challenges in computing resources, energy consumption and training costs.

2. Modular and combo-structure

   • Transform from a single large model to a modular, composable architecture
   • Specialized modal expert model working together
   • Combining modules with different capabilities on demand

   A modular architecture will improve the flexibility and scalability of the system, allowing the combination of different modules according to specific task requirements while reducing computing resource requirements. This direction is also conducive to the continuous update of the model and the expansion of capabilities.

3. Hybrid architecture innovation

   • Combining the advantages of different architectures such as Transformer, CNN, and GNN
   • Introducing new attention mechanisms and memory mechanisms
   • Exploring biologically inspired neural network architectures

   Hybrid architectures will take full advantage of different model structures to improve the performance of models on specific tasks while maintaining general capabilities. This innovation may lead to a significant improvement in model efficiency and capabilities.

Multimodal understanding and generation ability improvement

The future multimodal large language model will make major breakthroughs in understanding and generation capabilities, and achieve deeper multimodal intelligence.

Main trends：

1. Deep semantic understanding

   • Development from surface correlation to deep causal understanding
   • Ability to understand implicit information and contextual dependencies
   • Master complex abstract concepts and relationships

   Deep semantic understanding will enable models to grasp the essential connections between different modal information, rather than just the statistical correlations on the surface, thus showing stronger abilities in complex reasoning and problem solving.

2. Multimodal reasoning ability

   • Complex logical reasoning between different modes
   • Combining visual and linguistic information to solve problems
   • Dealing with counterfactual and hypothetical issues

   Enhanced reasoning capabilities will enable models to handle complex tasks that require synthesis of multiple sources of information, such as visual question-and-answer, scenario understanding, and decision support, demonstrating a closer thought process to humans.

3. Creative generation ability

   • Generate highly innovative and original multimodal content
   • Understand and apply aesthetic principles and creative rules
   • Adjust the creative style according to the context and intent

   The improvement of creative generation capabilities will make the model a stronger creative assistant, able to provide valuable support in the fields of artistic creation, design, content generation, etc., and even create new forms that are unimaginable to humans.

Improved efficiency and accessibility

The future multimodal large language model will be more efficient and easier to be widely accessed and used.

Main trends：

1. Computational efficiency optimization

   • Develop more efficient training and inference algorithms
   • Popularization of hardware-specific accelerators
   • Breakthroughs in model compression and quantization technology

   Improved computing efficiency will reduce the operating cost and energy consumption of models, allowing more powerful models to run on a wider range of devices, including mobile devices and edge computing environments.

2. Small and efficient multimodal model

   • Develop models with small parameters but strong performance
   • Lightweight models optimized for specific application scenarios
   • The application of knowledge distillation and model compression technology

   Small and efficient models will make multimodal AI capabilities easier to integrate into various applications and devices, expanding the application range of technology and lowering the threshold for use.

3. Open source ecosystem development

   • The emergence of more high-quality open source multimodal models
   • Improvement of developer tools and frameworks
   • Community-driven innovation and optimization

   The development of the open source ecosystem will promote the democratization and innovation of technology, allowing more developers and researchers to participate in the development and application of multimodal AI, and accelerate technological progress and application expansion.

Integration of emerging technologies

The multimodal large language model will be deeply integrated with other emerging technologies to create more powerful intelligent systems and applications.

Combining multimodal and reinforcement learning

The combination of reinforcement learning and multimodal large language models will create intelligent systems that can interact with the environment and learn from experience.

Main trends：

1. Vision-language-based decision-making system

   • Combining visual understanding and linguistic reasoning for decision-making
   • Continuously optimize decision-making strategies through environmental feedback
   • Applied in autonomous driving, robot control and other fields

   This combination will enable AI systems to make smarter decisions in complex real-world environments, understand the state of the environment and take appropriate actions, while interpreting their decision-making processes.

2. Multimodal interactive learning

   • Continuous learning through multimodal feedback
   • Learn from human demonstration and guidance
   • Adapt to user preferences and environmental changes

   Interactive learning will enable models to continuously improve based on user feedback and environmental changes, provide more personalized and adaptable services, and establish more natural human-computer collaboration relationships.

3. Independent exploration and knowledge acquisition

   • Actively explore the environment to acquire new knowledge
   • Identify knowledge gaps and seek filling
   • Build and update internal knowledge representations

   The ability of autonomous exploration will enable the model to no longer rely solely on pre-trained data, but to actively acquire new information, keep knowledge updated and expanded, and cope with the ever-changing world.

Fusion of multimodal and neural symbolic systems

The combination of neural symbolic methods and multimodal large language models will bring about significant improvements in reasoning ability and interpretability.

Main trends：

1. Multimodal reasoning for symbolic guidance

   • Combining the perception ability of neural networks and the reasoning ability of symbolic systems
   • Use logical rules to guide multimodal understanding
   • Improve the accuracy and reliability of complex inference tasks

   This fusion will overcome the limitations of pure neural network approaches in strict logical reasoning while maintaining the ability to process unstructured multimodal data and achieving stronger problem-solving capabilities.

2. Interpretable multimodal system

   • Provides symbolic interpretation of decision-making and generation processes
   • Making the reasoning process understandable and verifiable to humans
   • Supports interactive error correction and improvement

   Improved interpretability will enhance users' trust in the system, allowing professionals to better collaborate with AI systems and meet regulatory and audit requirements in key areas.

3. Multimodal understanding with knowledge graph enhancement

   • Utilize structured knowledge to guide multimodal content understanding
   • Integrate perceived information with existing knowledge
   • Supports background knowledge-based reasoning

   Knowledge enhancement will enable models to utilize structured knowledge already in humans to make up for the shortcomings of pure data-driven methods and demonstrate deeper understanding in the professional field.

Combining multimodal and brain-computer interface technology

The combination of multimodal large language model and brain-computer interface technology will create a new paradigm for human-computer interaction.

Main trends：

1. Direct thinking to multimodal content conversion

   • Convert brain signals into text, images, or other forms of content
   • Direct control of multimodal systems through thinking
   • Provide new ways to express and create for those with mobility difficulties

   This combination will create entirely new ways of interaction, allowing humans to more directly transform their thoughts into various forms of content, improving communication efficiency and possibilities.

2. Enhance cognitive ability

   • AI-assisted information processing and decision-making
   • Real-time multimodal information enhancement
   • Expand human memory and cognitive abilities

   Cognitive enhancement will enable humans to process and understand complex information more effectively, make up for cognitive limitations, and provide support in education, professional work and daily life.

3. Emotional and Intentional Understanding

   • Combining brain signals and multimodal inputs to understand user emotions
   • Forecast user intentions and needs
   • Provide highly personalized response and service

   Emotional and intentional understanding will make human-computer interaction more natural and intuitive, and the system can understand the implicit needs and emotional states, providing more considerate services and support.

Application field expansion

The application field of multimodal large language model will continue to expand and penetrate into more industries and life scenarios.

Breakthrough in the field of health care

The application of multimodal large language model in the field of medical and health will make major breakthroughs, bringing changes in medical services and health management.

Main trends：

1. Multimodal medical diagnostic system

   • Integrate medical imaging, clinical text and physiological data for diagnosis
   • Provide detailed diagnostic explanations and suggestions
   • Supports diagnosis of rare diseases and complex cases

   These systems will serve as powerful assistants to doctors, improving diagnostic accuracy and efficiency, especially in areas with limited resources and complex and difficult cases.

2. Personalized health management

   • Analyze multi-source health data to provide personalized suggestions
   • Predict health risks and propose preventive measures
   • Adapt to personal living habits and health goals

   Personalized health management will make preventive medicine and health maintenance more accurate and effective, helping individuals actively manage health and reduce disease risks.

3. Medical education and training innovation

   • Create highly interactive medical education content
   • Simulate various clinical scenarios for training
   • Provide personalized learning paths and feedback

   The innovation of medical education will improve the training quality and efficiency of medical professionals, accelerate knowledge updates, and ultimately improve the overall medical service level.

Education and lifelong learning change

The multimodal large language model will profoundly change the way of education and learning and create a more personalized and effective learning experience.

Main trends：

1. Super personalized learning experience

   • Customize content based on learners’ abilities, style and goals
   • Real-time adjustment of difficulty and teaching methods
   • Provide multimodal learning materials and feedback

   Hyper-personalized learning will enable every learner to get the most suitable educational experience, improve learning efficiency and results, while enhancing learning motivation and interest.

2. Immersive multimodal learning environment

   • Create a learning environment that blends text, images, audio and interactions
   • Simulate real scenes for practical learning
   • Provide instant feedback and guidance

   An immersive learning environment will make abstract concepts concrete and understandable, enhancing memory and understanding through multi-sensory experiences, especially suitable for the learning of complex skills and knowledge.

3. Lifelong learning support system

   • Help identify knowledge gaps and learning opportunities
   • Recommend personalized learning paths
   • Integrate new knowledge with existing knowledge system

   Lifelong learning support will help people maintain knowledge renewal and skills development in a rapidly changing world, adapting to career changes and personal growth needs.

Creative Industry and Cultural Innovation

The multimodal large language model will bring revolutionary changes to the creative industry and create new art forms and cultural expression methods.

Main trends：

1. Collaborative creative tools

   • In-depth collaboration between AI and human creators
   • Provide creative inspiration and technical support
   • Expand creators' expression skills and efficiency

   Collaborative creative tools will change the creative process, allowing creators to more freely explore creative possibilities, overcome technical limitations, and achieve richer artistic expression.

2. New multimodal art form

   • Novel art forms that combine multiple modalities such as text, images, music, etc.
   • Interactive and adaptive artistic experience
   • Artistic expression across cultures and languages

   The new art form will expand the boundaries of art, create unprecedented expressions and experiences, and enrich human cultural life and spiritual world.

3. Cultural Heritage Protection and Dissemination

   • Digitalization and reconstruction of historical and cultural heritage
   • Create immersive historical and cultural experiences
   • Make ancient cultures pass on and spread in a modern way

   Cultural heritage work will enable better protection and wider dissemination of precious history and culture, enhance cultural identity and understanding, and promote cultural diversity.

Social impact and ethical considerations

The development of multimodal large language model will have a profound impact on society, and will also bring a series of ethical challenges and considerations.

Job and Employment Change

Multimodal AI technology will reshape the job market and working methods, creating new opportunities while also bringing challenges.

Main trends：

1. Redefinition of job roles

   • Switching from repetitive tasks to creative and strategic work
   • Human-computer collaboration has become the mainstream working mode
   • The emergence of new job roles and careers

   Changes in job roles will require the labor market to adapt to new skills needs, and education and training systems also need to be adjusted accordingly to cultivate talents adapted to the AI ​​era.

2. Creative and knowledge work transformation

   • AI-assisted creative and knowledge production
   • The role of content creators has changed from producer to planner
   • Personalization and scale of professional services

   The transformation of creative and knowledge work will change the way value is created in these areas, potentially leading to a significant increase in productivity while also challenging traditional professional identities and values.

3. Skills Needs and Educational Change

   • Increased demand for advanced cognitive and social-emotional abilities
   • Continuous learning and adaptability become more important
   • Educational systems need to adapt to new skills needs

   Changes in skills requirements will drive reforms in the education system, emphasizing the cultivation of unique human abilities complementary to AI, such as creativity, critical thinking, emotional intelligence and moral judgment.

Changes in information ecosystem

The multimodal large language model will profoundly change the way information is created, disseminated and consumed, and reshape the information ecosystem.

Main trends：

1. Democratizing content creation

   • Lower the technical barriers for content creation
   • Enable more people to express their ideas and ideas
   • Explosive growth in content form and quantity

   The democratization of content creation will make the information ecosystem more diverse and rich, but it will also bring challenges to content quality and authenticity, requiring new content evaluation and screening mechanisms.

2. Information authenticity and credibility challenges

   • Blurred boundaries between generated content and real content
   • Increased risk of deep falsification and misleading information
   • The importance of information verification and traceability is enhanced

   The challenge of information authenticity will require the development of stronger content verification technologies, establishing new trust mechanisms, and improving the public's media literacy and critical thinking skills.

3. Personalized information experience

   • Highly customized information push and content presentation
   • Cross-modal information integration and display
   • The balance between information cocoon and diverse perspectives

   Personalized information experience will improve the efficiency and relevance of information acquisition, but it also brings challenges to information diversity and social consensus, and requires a balance between personalized and shared public discourse.

Potential breakthrough technology

Some breakthrough technologies may appear in the future to completely change the capabilities and application methods of multimodal large language models.

Independent learning and continuous evolution

Future multimodal systems may have the ability to learn independently and evolve continuously, continuously improve their own performance and adapt to new environments.

Potential breakthrough：

1. The leap of self-supervised learning

   • Learn from very small amounts of labeled data
   • Automatically discover structures and patterns in data
   • Continuously update knowledge from new data

   The breakthrough in self-supervised learning will greatly reduce the dependence on labeled data, allowing the model to more effectively utilize massive unlabeled data and keep knowledge updated and expanded.

2. Meta-learning and rapid adaptation

   • Learn how to learn new tasks and areas
   • Quickly master new skills from a few examples
   • Migrate knowledge between different environments and tasks

   Meta-learning ability will make the model more adaptable and flexible, able to quickly respond to new situations and needs, and reduce dependence on specialized training.

3. Autonomous architecture search and optimization

   • Automatically discover the optimal model architecture
   • Adjust network structure according to task requirements
   • Continuously optimize computing efficiency and performance

   The capability of autonomous optimization will accelerate model innovation, discover architectures and methods that human designers may ignore, while improving resource utilization efficiency.

Multimodal General Intelligence

The multimodal large language model may develop towards a more general form of artificial intelligence, showing understanding and reasoning skills closer to humans.

Potential breakthrough：

1. Cross-modal causal reasoning

   • Understand the causal relationship between different modalities
   • Conduct counterfactual reasoning and hypothesis testing
   • Building a multimodal world model

   Causal reasoning capabilities will enable models to move beyond surface correlations, understand the mechanisms behind phenomena, and support deeper understanding and more reliable predictions.

2. Multimodal common sense understanding

   • Master basic knowledge of humans
   • Understand the basic laws of the physical world
   • Grasp the implicit rules of social interaction

   Common sense understanding will enable the model to process implicit information, make common sense inferences, avoid obvious errors, and behave more naturally and rationally in complex environments.

3. Multimodal long-term memory and planning

   • Maintain long-term consistent representation of knowledge
   • Perform multi-step reasoning and planning
   • Learn and apply from past experience

   Long-term memory and planning capabilities will enable models to handle tasks that require continuous interaction and long-term consistency, such as complex problem solving, long-term dialogue, and collaborative projects.

Human-computer symbiosis system

In the future, deeper human-computer symbiosis systems may emerge to achieve complementary and coordinated enhancement of the advantages of humans and AI.

Potential breakthrough：

1. Intent understanding and collaborative creation

   • Deep understanding of human intentions and goals
   • Proactively provide relevant support and suggestions
   • Collaborate with human creators to complete complex tasks

   Intent understanding will make human-computer collaboration more natural and efficient, and AI systems can predict needs and provide just the right support to become a true creative partner.

2. Enhance cognitive and decision-making support

   • Expand human cognitive abilities and memory
   • Provide multi-angle analysis and suggestions
   • Helps identify blind spots and biases

   Cognitive enhancement will help humans process complex information and decisions beyond individual capabilities while maintaining human dominance in value judgments and ultimate decisions.

3. Emotional intelligence and social interaction

   • Understand and respond to human emotions
   • Provide emotional support and companionship
   • Promote interpersonal communication and social connection

   Emotional intelligence will enable AI systems to connect with humans at an emotional level, provide more comprehensive support, and possibly help solve social problems such as loneliness and social isolation.

The future development of multimodal large language model is full of infinite possibilities. It will continue to promote innovation in the field of artificial intelligence, change the way humans interact with technology, and have a profound impact on all aspects of society. With the advancement of technology and the expansion of applications, we need to work together to ensure that the development direction of this powerful technology is in line with the long-term interests and values ​​of mankind and serve to create a better future.

in conclusion

Multimodal Large Language Models (MLLMs), as cutting-edge technologies in the field of artificial intelligence, are developing at an unprecedented rate and profoundly changing the way we interact with technology. Through the in-depth discussion of this research report, we can clearly see the development trajectory, current status, technical architecture, application scenarios, challenges faced and future development trends of this technology.

The development history of multimodal large language model demonstrates the natural evolution of artificial intelligence from single mode to multimodal fusion. From the early independent visual and language models to the comprehensive system that can simultaneously understand and generate multiple modal content such as text, images, audio, etc., this evolution process has embodied the wisdom and efforts of many researchers and engineers. The emergence of representative models such as GPT-4V, Claude 3, Gemini, and Wen Xin Yiyan marks that the multimodal large language model has entered the stage of practicalization and has shown huge application potential in various fields.

From the perspective of technical architecture, the multimodal large language model mainly adopts a Transformer-based architecture, and through various innovative modal fusion methods, it realizes effective integration and interaction of information in different modalities. The development of key technologies such as pre-training-fine-tuning paradigm, cross-modal alignment technology, and multimodal representation learning provides strong understanding and generation capabilities for the model. However, technical difficulties such as modal alignment and fusion problems, computing resources and efficiency problems, data quality and diversity challenges still exist, and researchers need to continue to explore more effective solutions.

In terms of application scenarios, the multimodal large language model has shown strong capabilities in content creation, multimodal dialogue, visual question-and-answer, cross-modal retrieval and other fields. At the same time, the application in vertical fields such as medical and health, education and training, autonomous driving, and cultural creativity is also constantly deepening, creating new values ​​and possibilities. These applications not only improve efficiency and convenience, but also create new ways of interaction and service, which has a profound impact on human society.

However, the development of multimodal large language models also faces a series of challenges and limitations. At the technical level, the robustness, generalization ability and computing efficiency of the model still need to be improved; at the ethical and social level, issues such as bias and fairness, privacy and security risks, and social impact need to be taken seriously; at the regulatory and legal level, challenges such as intellectual property issues, responsibility and accountability mechanisms, international coordination and standardization also need to be solved urgently. These challenges require technological innovation, policy development and joint efforts of multistakeholders.

Looking ahead, multimodal large language models will continue to develop in the direction of larger scale, higher efficiency and stronger capabilities. Innovation in model architecture, improvement of multimodal understanding and generation capabilities, and improvement of efficiency and accessibility will drive continuous technological progress. At the same time, the integration with emerging technologies such as reinforcement learning, neural symbol systems, and brain-computer interfaces will create more powerful intelligent systems and applications. The application in the fields of medical health, education and learning, creative industries will be further deepened, bringing more innovation and changes.

The development of multimodal large language model will have a profound impact on society, including job and employment changes, information ecosystem changes, etc. Potential breakthrough technologies, such as independent learning and continuous evolution, multimodal general intelligence, human-computer symbiosis systems, may completely change the relationship between humans and technology and create new possibilities.

In short, as an important development direction in the field of artificial intelligence, multimodal large language models are developing at an astonishing rate and will profoundly change our lives, work and society. Faced with the huge potential and challenges of this technology, we need to maintain an open, prudent and responsible attitude, and work together to ensure that the direction of technological development is in line with the long-term interests and values ​​of mankind, and serve to create a better future.

References

1. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

2. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748-8763). PMLR.

3. OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.

4. Anthropic. (2023). Claude: A Family of Foundation Language Models. Technical Report.

5. Google. (2023). Gemini: A Family of Highly Capable Multimodal Models. Technical Report.

6. Baidu. (2023). Wen Xin Yiyan Technical Report. Technical Report.

7. Li, J., Li, D., Savarese, S., & Hoi, S. (2023). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. arXiv preprint arXiv:2301.12597.

8. Alayrac, J. B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., ... & Zisserman, A. (2022). Flamingo: a Visual Language Model for Few-Shot Learning. Advances in Neural Information Processing Systems, 35.

9. Lu, J., Clark, S., Zellers, R., Mottaghi, R., & Kembhavi, A. (2022). Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks. arXiv preprint arXiv:2206.08916.

10. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

11. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

12. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623).

13. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2021). Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300.

14. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... & Girshick, R. (2023). Segment anything. arXiv preprint arXiv:2304.02643.

15. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125.

16. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).

17. Zhu, Y., Du, Y., Garbacea, C., Zhuang, Y., Poesia, G., Savarese, S., & Niebles, J. C. (2023). Visual instruction tuning. arXiv preprint arXiv:2304.08485.

18. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., ... & Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.

19. Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., ... & Sifre, L. (2022). Training compute-optimal large language models. arXiv preprint arXiv:2203.15556.

20. Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., ... & Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.

21. Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., ... & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.

22. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

23. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1), 5485-5551.

24. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

25. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

26. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.

27. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., ... & Lample, G. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.

28. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., ... & Fiedel, N. (2022). Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.

29. Jiang, Y., Natarajan, V., Chen, X., Rohrbach, M., Batra, D., & Parikh, D. (2018). Pythia v0. 1: the winning entry of the vqa challenge 2018. arXiv preprint arXiv:1807.09956.

30. Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017). Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6904-6913).