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 \modelname (\modelmeaning) 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.

翻译：近期，将外部工具与大型语言模型（LLMs）集成的研究取得了新进展，在数学推理、代码生成和智能助手等领域展现出广泛应用。然而，现有方法依赖简单的一次性检索策略，在有效且准确地筛选相关工具方面仍存在不足。本文提出了一种创新的“模型名（模型含义）”方法，涵盖“规划与检索（P&R）”和“编辑与落地（E&G）”两种范式。P&R范式包含一个用于筛选相关工具的神经检索模块和一个基于LLM的查询规划器，可将复杂查询分解为可操作的任务，从而提升工具利用的有效性。E&G范式则利用LLM根据用户场景丰富工具描述，弥合用户查询与工具功能之间的鸿沟。实验结果表明，这些范式在工具检索任务中显著提高了召回率和NDCG指标，大幅超越了当前最优模型。