Summary of Learning Evolving Tools For Large Language Models, by Guoxin Chen et al.
Learning Evolving Tools for Large Language Models
by Guoxin Chen, Zhong Zhang, Xin Cong, Fangda Guo, Yesai Wu, Yankai Lin, Wenzheng Feng, Yasheng Wang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ToolEVO, a framework that enhances the adaptive and reflective capabilities of large language models (LLMs) in dynamic environments. Traditional approaches overlook the issue of tool variability, making it challenging for LLMs to correctly invoke tools. ToolEVO leverages Monte Carlo Tree Search to facilitate active exploration and interaction within dynamic environments, allowing for autonomous self-reflection and self-updating of tool usage based on environmental feedback. The authors also introduce ToolQA-D, a benchmark designed to evaluate the impact of tool variability. Extensive experiments demonstrate the effectiveness and stability of the approach, emphasizing the importance of adaptability to tool variability for effective tool learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re using a super smart computer program that can learn new things and do tasks on its own. This program is called a large language model (LLM). But what if the things it’s trying to do need other tools or programs to work? These tools might change over time, making it hard for the LLM to use them correctly. Most research on this topic focuses on simple situations where everything stays the same. However, in real life, things are always changing! This paper proposes a new way to make these super smart computer programs more flexible and able to adapt to changing situations. They call this approach ToolEVO. It’s like having a special kind of GPS that helps the program figure out what tools it needs and how to use them correctly. |
Keywords
» Artificial intelligence » Large language model
