Summary of Enhancing Tool Retrieval with Iterative Feedback From Large Language Models, by Qiancheng Xu et al.
Enhancing Tool Retrieval with Iterative Feedback from Large Language Models
by Qiancheng Xu, Yongqi Li, Heming Xia, Wenjie Li
First submitted to arxiv on: 25 Jun 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 an innovative method for enhancing large language models’ capabilities by learning from external tools. Current approaches have shown promising results, but the real-world scenario is often characterized by a vast number of tools with irregular updates, emphasizing the need for a dedicated tool retrieval component. The proposed approach addresses challenges such as complex user instructions and misalignment between tool retrieval and usage models. Specifically, it utilizes iterative feedback from the large language model to progressively improve the tool retriever’s understanding of instructions and tools. Experiments indicate that this approach achieves advanced performance in both in-domain and out-of-domain evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps big computers learn new things by adding more tools to their toolbox. Right now, these computers can get better at some tasks when they’re taught with extra tools. But real life is messy, and there are many tools that need to be learned about. To solve this problem, the researchers came up with a way to make the computer give feedback to itself as it learns about new tools. This helps the computer understand how to use the tools better. The team tested their idea and found that it works really well. |
Keywords
» Artificial intelligence » Large language model
