In today's technology field, the development of big models and AI technologies is changing with each passing day. When you are first learning big models, you will definitely encounter various professional terms, which are dazzling. This article will briefly explain some key terms to help you better understand the world of big models and AI during your learning process.
Large Language Model (LLM, Large Language Model)
The large language model is a large-scale neural network model based on deep learning, usually using the Transformer architecture. It can process large amounts of language data and generate high-quality text, and learn complex patterns of language through large-scale dataset training. For example, the GPT series and BERT are both famous large language models that perform well in natural language processing tasks, capable of complex dialogue, text creation, etc.
Transformer
Transformer is a neural network architecture widely used in natural language processing tasks. Because of its self-attention mechanism, it can efficiently process long-distance dependencies in sequence data, becoming the mainstream architecture in the NLP field. Just like the Lego master in the AI world, we find the relationship between words through the "attention mechanism", for example, when reading detective novels, we automatically mark the key clues of "murderer" and "murder weapon".
RNN-Recurrent Neural Network
RNN is a neural network architecture that can process sequence data, suitable for tasks such as natural language processing. Although effective, there are limitations in capturing long-term dependencies, and the problem of gradient disappearance or explosion is prone to problems.
LSTM-Long Short-Term Memory
LSTM is a special type of RNN. Through a special gating mechanism, the gradient disappearance problem of standard RNNs in long sequence training is solved, thereby better capturing long-term dependencies.
CNN-Convolutional Neural Network
CNN is a neural network architecture specially used to process image data, extracting image features through convolutional operations. In addition, CNN can also be applied to other fields such as text classification.
Prompt
Enter the prompt word for the AI model. In AI large models, it is used to guide the model to generate context information or instructions for a specific type of output. For example, telling the model to "write three-line poems in Li Bai's style, the theme is autumn milk tea", just like the "magic spell" design technique that talks to AI.
Prompt Engineering-Tip Engineering
Technology to design and optimize the process of input prompts to improve the output effect of artificial intelligence models. Tips are carefully designed with clear instructions, relevant context, specific examples, and accurate inputs to guide the large language model to generate high-quality output that meets expectations.
RAG-Retrieval-Augmented Generation
RAG is an artificial intelligence technology that combines information retrieval technology and language generation model. It enhances the model's ability to handle knowledge-intensive tasks such as question and answer, text summary, content generation, etc. by retrieving relevant information from the external knowledge base and inputting it as prompts to a large language model.
Vector Database
Vector database is a database system specially used to store, retrieve and manage high-dimensional vector data. Its core capability is to quickly perform vector similarity searches, and can quickly find the vector that is most similar to the target vector from a large number of high-dimensional vectors.
Vector similarity search
Vector similarity search is to measure their similarity by calculating the distance between vectors. Commonly used distance measurement methods include Euclidean distance, cosine similarity, dot product, etc.
Hopefully the explanation of this article will help you better understand these terms and provide a reference for your study and work. After all, there are only a few simple sentences, but they are not well-studded with pictures and texts. If you want to deeply understand the links and roles, you still need to read other materials. The development of big models and AI technology has brought us unprecedented opportunities. We hope that you can actively participate and jointly promote technological progress.