Researchers from DeepMind, Princeton University and Stanford University recently proposed an innovative framework called LATM (LLMs As Tool Makers), which can integrate large language models like GPT-4 Transform into a tool maker (Note: That is, let the model itself generate software tools to handle new tasks).
▲ Picture source Arxiv
The innovation of LATM is that it introduces the concept of tool making and usage to the language model, enabling the model to autonomously 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 consists of the following two key parts:
Toolmaking: As a toolmaker, LLM specializes in designing software tools for specific tasks, which are implemented in the form of Python functions.
Tool usage: Another LLM acts as a tool consumer and can invoke these software tools to handle new requests.This design allows LATM to assign tasks to the most suitable LLM, and the tool manufacturing process that requires high-intensity computing power can be assigned to a powerful, resource-intensive model, such as GPT-4; The use of tooling processes is assigned to lightweight, cost-effective models such as GPT-3.5 Turbo. This approach not only enhances the problem-solving ability of LLM, but also significantly reduces the average computational cost of processing a series of tasks to maximize the efficiency of the framework.
▲ Picture source Arxiv
Experimental results verify the effectiveness of LATM on complex reasoning tasks. The researchers found that LATM was able to achieve comparable performance to resource-intensive models and be more cost-effective. This large-scale language model approach mimics the evolutionary process of human creation and use of tools, which can promote the development of large-scale language model generation tools.
The 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 to accelerate the development of natural language processing and artificial intelligence.
