Planning and Editing What You Retrieve for Enhanced Tool Learning
arxiv(2024)
摘要
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 () 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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