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    <abstract>Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing “Plan-and-Retrieve (P&amp;R)” and “Edit-and-Ground (E&amp;G)” paradigms. The P&amp;R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&amp;G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T Planning and Editing What You Retrieve for Enhanced Tool Learning
%A Huang, Tenghao
%A Jung, Dongwon
%A Kumar, Vaibhav
%A Kachuee, Mohammad
%A Li, Xiang
%A Xu, Puyang
%A Chen, Muhao
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F huang-etal-2024-planning
%X Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing “Plan-and-Retrieve (P&R)” and “Edit-and-Ground (E&G)” paradigms. The P&R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.
%U https://aclanthology.org/2024.findings-naacl.61
%P 975-988

Markdown (Informal)

[Planning and Editing What You Retrieve for Enhanced Tool Learning](https://aclanthology.org/2024.findings-naacl.61) (Huang et al., Findings 2024)

• Planning and Editing What You Retrieve for Enhanced Tool Learning (Huang et al., Findings 2024)

ACL

• Tenghao Huang, Dongwon Jung, Vaibhav Kumar, Mohammad Kachuee, Xiang Li, Puyang Xu, and Muhao Chen. 2024. Planning and Editing What You Retrieve for Enhanced Tool Learning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 975–988, Mexico City, Mexico. Association for Computational Linguistics.