Planning And Editing What You Retrieve For Enhanced Tool Learning
Huang Tenghao, Jung Dongwon, Chen Muhao. Arxiv 2024
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.
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