Multi-Field Tool Retrieval
Yichen Tang, Weihang Su, Yiqun Liu, Qingyao Ai

TL;DR
This paper introduces Multi-Field Tool Retrieval, a framework that improves the accuracy and robustness of retrieving external tools for LLMs by modeling multiple aspects of tool utility, addressing key challenges in documentation and query matching.
Contribution
It proposes a novel multi-field modeling approach for tool retrieval, significantly enhancing performance and generalizability over existing ad-hoc methods.
Findings
Achieves state-of-the-art results on five datasets and a mixed benchmark.
Demonstrates superior robustness and generalizability.
Effectively aligns user intent with tool representations.
Abstract
Integrating external tools enables Large Language Models (LLMs) to interact with real-world environments and solve complex tasks. Given the growing scale of available tools, effective tool retrieval is essential to mitigate constraints of LLMs' context windows and ensure computational efficiency. Existing approaches typically treat tool retrieval as a traditional ad-hoc retrieval task, matching user queries against the entire raw tool documentation. In this paper, we identify three fundamental challenges that limit the effectiveness of this paradigm: (i) the incompleteness and structural inconsistency of tool documentation; (ii) the significant semantic and granular mismatch between user queries and technical tool documents; and, most importantly, (iii) the multi-aspect nature of tool utility, that involves distinct dimensions, such as functionality, input constraints, and output…
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
TopicsTopic Modeling · Natural Language Processing Techniques · Semantic Web and Ontologies
