Summary of From Exploration to Mastery: Enabling Llms to Master Tools Via Self-driven Interactions, by Changle Qu et al.
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions
by Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen
First submitted to arxiv on: 10 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 A novel framework called DRAFT is proposed to dynamically refine tool documentation for Large Language Models (LLMs) interacting with external environments. The framework addresses the comprehension gap between LLMs and tools due to inadequate and inaccurate human-centric documentation. DRAFT involves three learning phases: experience gathering, learning from experience, and documentation rewriting, which are optimized by a diversity-promoting exploration strategy and tool-adaptive termination mechanism. Experimental results on multiple datasets demonstrate that DRAFT significantly improves documentation quality, fostering effective utilization of tools by LLMs. The refined documentation shows robust cross-model generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can interact with external environments using tools, but they need good instructions to do so effectively. This paper solves a big problem: how to make tool documentation better for LLMs. They propose a new way to refine documentation by letting the LLM learn from its experiences and improve the documentation over time. The new approach works well and helps LLMs use tools more effectively. |
Keywords
» Artificial intelligence » Generalization
