ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, Bin Wang

TL;DR
ToolRerank is a novel reranking method that adaptively and hierarchy-aware refines tool retrieval for language models, improving performance especially with large and diverse tool libraries.
Contribution
It introduces adaptive truncation and hierarchy-aware reranking to enhance tool retrieval, addressing unseen tools and hierarchical structures.
Findings
Improves retrieval quality for seen and unseen tools
Enhances LLM execution results with better tool selection
Balances diversity and concentration in retrieval results
Abstract
Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address this challenge, previous studies have proposed retrieving suitable tools for the LLM based on the user query. However, previously proposed methods do not consider the differences between seen and unseen tools, nor do they take the hierarchy of the tool library into account, which may lead to suboptimal performance for tool retrieval. Therefore, to address the aforementioned issues, we propose ToolRerank, an adaptive and hierarchy-aware reranking method for tool retrieval to further refine the retrieval results. Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Advanced Database Systems and Queries · Data Quality and Management
MethodsLib
