Summary of Ptr: Precision-driven Tool Recommendation For Large Language Models, by Hang Gao et al.
PTR: Precision-Driven Tool Recommendation for Large Language Models
by Hang Gao, Yongfeng Zhang
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel approach to precision-driven tool recommendation (PTR) for Large Language Models (LLMs), enhancing their capacity to solve complex problems. By augmenting LLMs with external tools, the authors aim to provide them with an optimal set of tools tailored to specific tasks, considering both quantity and quality. Current approaches focus on refining ranking lists, but often fail to equip LLMs with the best toolset prior to execution, leading to inefficiencies like redundant or unsuitable tools. The proposed PTR approach captures initial tool sets by leveraging historical usage and dynamically adjusts them through multi-view-based tool addition. A new dataset, RecTools, and metric, TRACC, are introduced to evaluate tool recommendation effectiveness. Comprehensive experiments demonstrate promising accuracy across open benchmarks and the RecTools dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have become very good at solving complex problems, but they still need some help from other tools. The problem is that there are many of these external tools, so it’s hard to know which ones to use for a specific task. Some people try to rank all the tools and then pick the top few, but this doesn’t always work because different tasks might need different numbers of tools. This paper tries to solve this problem by coming up with a new way to recommend just the right tools for LLMs. They test their idea on some datasets and it looks like it works pretty well. |
Keywords
» Artificial intelligence » Precision
