Shulou(Shulou.com)11/24 Report--

CTOnews.com, May 31 / PRNewswire-Asianet /-- researchers at DeepMind, Princeton University and Stanford University recently proposed an innovative framework called LATM (LLMs As Tool Makers) that turns large language models like GPT-4 into tool makers. (CTOnews.com Note: let the model generate its own software tools to handle new tasks).

The innovation of ▲ graph source ArxivLATM is that it introduces the concept of tool manufacturing and use to the language model, so that the model can independently create tools suitable for different tasks. This framework can not only improve the flexibility and adaptability of the model, but also provide a more efficient and economical solution for generative AI. The framework mainly includes the following two key parts:

Tool manufacturing: as a tool maker, LLM designs software tools for specific tasks, which are implemented in the form of Python functions.

Tool usage: another LLM as a tool consumer can call these software tools to process new requests.

This design allows LATM to assign tasks to the most appropriate LLM, and tool manufacturing processes that require high computing power can be assigned to powerful, resource-intensive models, such as GPT-4;, while relatively simple tool processes are assigned to lightweight, cost-effective models, such as GPT-3.5 Turbo. This method not only enhances the problem-solving ability of LLM, but also significantly reduces the average computing cost of handling a series of tasks to maximize the efficiency of the framework.

The experimental results of ▲ source Arxiv verify the effectiveness of LATM in complex reasoning tasks. The researchers found that LATM can achieve performance comparable to resource-intensive models and is more cost-effective. This large-scale language modeling method imitates the evolutionary process of human creation and use of tools, and can promote the development of large-scale language model generation tools.

Researchers believe that LATM will bring a major breakthrough in the field of natural language processing and play an important role in practical applications. This innovation can effectively improve the efficiency of generative AI and accelerate the development of natural language processing and artificial intelligence.

CTOnews.com 's friends can find the paper information here.

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