Large Language Models as Tool Makers
Large Language Models as Tool Makers
cross-posted from: https://lemmy.intai.tech/post/124795
Large Language Models as Tool Makers Authors: Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, Denny Zhou
Word count: 4579 words
Estimated read time: 12 minutes
Source code: https://github.com/ctlllll/LLM-ToolMaker ↗
Summary:
This paper proposes a framework called LLMs As Tool Makers (LATM) that enables large language models (LLMs) to create and utilize their own tools for solving complex reasoning tasks. The key idea is to separate the process into two stages - tool making and tool using. In the tool making stage, a powerful yet expensive LLM acts as the "tool maker" to generate reusable Python functions for solving demonstrations of a task. In the tool using stage, a lightweight and cost-effective LLM acts as the "tool user" to call these tools to solve new instances of the task.
Experiments on tasks like logical deduction, tracking shuffled objects, Dyck language parsing, etc show that with tools made by GPT-4, GPT-3.5 Turbo as the tool user can match or exceed the performance of GPT-4 at lower cost. The authors also introduce a "dispatcher" LLM to handle streaming tasks by identifying when to reuse existing tools or request new ones.
Overall, this work demonstrates a promising approach to enabling LLMs to create their own tools, reducing reliance on human-crafted tools. The division of labor also allows using smaller models for most of the inferences, improving cost-efficiency. This technique could significantly expand the capabilities of LLMs in a scalable manner.
The proposed LATM framework demonstrates an interesting and promising approach to improving the reasoning and problem-solving capabilities of large language models in a cost-effective manner. Here are some thoughts on its applicability:
The ability for LLMs to create their own tools could be very useful for building practical applications. For any recurring task, the model could generate a reusable tool instead of solving from scratch each time. This could make applications more efficient and scalable.
The staged approach allows combining different sized models optimally - a powerful model makes tools, while lightweight models use the tools. This cost-effectiveness is attractive for real-world applications with budget constraints.
The tools being in Python allows them to integrate into application codebases easily. The dispatcher model also provides flexibility to handle new tasks.
The method's applicability does seem more geared towards logical reasoning, procedural and algorithmic tasks right now. Further research may be needed to extend it to other domains.
There are still open challenges around rigorously testing and validating the quality and safety of automatically generated tools. Methods to provide human oversight would be important.
Overall, the LATM paradigm does appear promising for augmenting LLMs and enabling them to participate more actively in their own learning and tooling. With further research to broaden its scope, it could become a general framework for efficiently enhancing LLM capabilities.
So in summary, LATM seems quite promising as a technique for unlocking more of the potential of LLMs for practical applications requiring complex reasoning in a scalable and cost-efficient manner. More research is still needed, but the principles demonstrated align well with enabling wider usage of LLMs and GANs in applications.