In today's digital wave, artificial intelligence technology has developed rapidly, and various models have emerged like mushrooms after a rain. With its unique advantages, the DeepSeek model has quickly emerged in the field of artificial intelligence and has become a highly anticipated new star.
The DeepSeek model is carefully crafted by Hangzhou DeepQuSuo Artificial Intelligence Basic Technology Research Co., Ltd. Since its introduction, it has attracted global attention with its outstanding performance and outstanding performance. In natural language processing tasks, it can accurately understand the complex semantics of human language, and can provide high-quality responses whether it is daily conversations, article writing or question answering. For example, in terms of text generation, it can generate articles with clear logic and rich content based on given topics and prompts, covering a variety of types such as news reports, story creation, technical documents, etc. In the intelligent customer service scenario, the DeepSeek model can quickly and accurately understand customer questions and provide professional and considerate answers, greatly improving the efficiency and quality of customer service. In the field of intelligent writing, it provides creators with rich inspiration and materials to help them complete their creative tasks more efficiently. It has also been widely used in many fields such as intelligent translation and data analysis, injecting strong impetus into the development of various industries.
In key scenarios of enterprise-level applications, some functional problems of the DeepSeek model have gradually emerged, which has formed certain obstacles to its widespread application and in-depth development. The current challenges are lack of function and instability.
DeepSeek-R1: Distress of missing functions
In practical applications, the Function Call function is like a bridge connecting artificial intelligence models with external tools or services. It allows the model to automatically call corresponding functions or tools according to user needs, thereby achieving more complex and intelligent task processing. For example, in the intelligent office scenario, when the user needs to query sales data on a specific date and generate a simple sales report, if the model supports the Function Call function, it can automatically call the database query function to obtain data, and then call the report generation tool. , quickly generate a sales report with standardized formats and accurate content. For example, in the intelligent customer service scenario, when a customer asks about the inventory status of a certain product, the model can call the interface of the inventory management system through Function Call to obtain accurate inventory information in real time and feed it back to the customer. The Json Output function can present the output results of the model in a standardized and structured format, which facilitates subsequent processing and analysis. In a data analysis scenario, after the model performs sentiment analysis on a large amount of text data, it outputs the analysis results in Json format through Json Output, and developers can easily import this data into data analysis tools for further mining and visual display.
However, the DeepSeek-R1 model does not support these two important functions for the time being, which makes it seem unscrupulous when facing some complex tasks. In the smart office scenario, the DeepSeek-R1 model cannot automatically call relevant tools to complete the tasks of data query and report generation. Users may need to perform these operations manually, which greatly increases the complexity and time cost of work and reduces work efficiency. In data analysis scenarios, due to the lack of Json Output functionality, the results of the model output may be in irregular text form. Developers need to spend a lot of time and effort to sort and format these results before they can perform subsequent analysis work. This is undoubtedly Increases the difficulty and workload of development.
V3 Model: Unstable Function Call
Although the V3 model supports the Function Call function, it has serious instability problems in actual use. Loop calling refers to the model falling into an infinite loop when calling a function, and constantly calling the same function or multiple functions repeatedly, resulting in a large amount of system resources being consumed, which may eventually lead to a program crash. The empty reply means that the model does not return any valid results after calling the function, which makes the user unable to obtain the required information and affects the user experience. This official has already explained in the documentation:/zh-cn/guides/function_calling
Tool use training and inference speed
Compared with Claude-3.5-Sonnet/GPT-4o, DeepSeek has a lack of tool use specialized training. Claude-3.5-Sonnet/GPT-4o has specially trained tool use, which makes it have a great advantage when doing agents. In smart office scenarios, Claude-3.5-Sonnet/GPT-4o can better call various office tools, such as document editing tools, table processing tools, etc., to achieve more efficient office task processing through tool use training. DeepSeek does not currently provide such specialized training, which limits its application in scenarios that require complex tool calls.
As an inference model, DeepSeek takes a long time to reason for answering questions, resulting in relatively slow answering questions. In online customer service scenarios, customers usually want to get a quick response. If DeepSeek takes a long time to think and answer questions, customers may lose patience due to the long wait time, thereby reducing customer satisfaction. In some scenarios with high requirements for real-time performance, such as instant messaging, real-time data analysis, DeepSeek's inference speed may also become a bottleneck in its application. In order to better apply the DeepSeek model in these scenarios, special optimizations are needed to improve its inference speed and responsiveness.
As an important participant in the field of artificial intelligence, the DeepSeek model has shown strong strength while also exposing some problems that need to be solved urgently. The lack and instability of Function Call and Json Output functions, as well as the inadequacy in tool use training and inference speed, have limited their wide application in enterprise-level applications and complex business scenarios to a certain extent.
However, facing these issues is the key to further development and improvement of the DeepSeek model. We have reason to believe that with the continuous advancement of technology and continuous investment in R&D, the DeepSeek model team is able to optimize and improve these issues. In the future, the DeepSeek model is expected to improve its performance in functions such as Function Call, Json Output and other functions through technological innovations, such as optimizing the model architecture, improving training algorithms, etc., making it more stable and efficient. In processing output results, it is also possible to use smarter algorithms and mechanisms to achieve reasonable display of the thinking process and final results, and improve the stability and accuracy of the reply. By enhancing tool use specialized training and optimizing inference algorithms, the DeepSeek model will also make significant progress in handling complex tasks and improving inference speed, thereby better meeting the needs of users in different scenarios.
With the rapid development of artificial intelligence technology, the DeepSeek model faces challenges and ushers in unprecedented opportunities. We look forward to the DeepSeek model that can continuously break through itself, solve existing problems, shine more dazzling in the field of artificial intelligence, and make greater contributions to promoting the intelligent development of various industries.