@inproceedings{braunschweiler-etal-2025-toolreagt,
title = "{T}ool{R}e{AG}t: Tool Retrieval for {LLM}-based Complex Task Solution via Retrieval Augmented Generation",
author = "Braunschweiler, Norbert and
Doddipatla, Rama and
Zorila, Tudor-catalin",
editor = "Zhang, Yuji and
Chen, Canyu and
Li, Sha and
Geva, Mor and
Han, Chi and
Wang, Xiaozhi and
Feng, Shangbin and
Gao, Silin and
Augenstein, Isabelle and
Bansal, Mohit and
Li, Manling and
Ji, Heng",
booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.knowllm-1.7/",
doi = "10.18653/v1/2025.knowllm-1.7",
pages = "75--83",
ISBN = "979-8-89176-283-1",
abstract = "Artificial intelligence agents when deployed to solve complex problems, need to first decompose the task into smaller manageable sub-tasks, and further associate tools if one is required to solve the sub-task. If the size of the set of tools to chose from is large, a retrieval system is usually employed to narrow down the tool choices before the LLM can proceed with associating tools to the sub-tasks. This paper focuses on the retrieval problem to identify the set of relevant tools to solve a complex task given a large pool of tools to chose from using retrieval augmented generation (RAG) and we refer to it as ToolReAGT. The proposed approach employs ReAct prompting to perform the retrieval in an iterative fashion to first identify if a tool is required and then associate one or more tools for each sub-task. This deviates from conventional RAG where an n-best list of tools are identified given the complex task directly. Experiments are presented on the UltraTool benchmark corpus with 1000 complex tasks and over 2000 tools to select from. A conventional RAG-system is established as baseline and compared to the ToolReAGt approach, resulting in an 8.9{\%} improved retrieval accuracy score recall@5."
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<abstract>Artificial intelligence agents when deployed to solve complex problems, need to first decompose the task into smaller manageable sub-tasks, and further associate tools if one is required to solve the sub-task. If the size of the set of tools to chose from is large, a retrieval system is usually employed to narrow down the tool choices before the LLM can proceed with associating tools to the sub-tasks. This paper focuses on the retrieval problem to identify the set of relevant tools to solve a complex task given a large pool of tools to chose from using retrieval augmented generation (RAG) and we refer to it as ToolReAGT. The proposed approach employs ReAct prompting to perform the retrieval in an iterative fashion to first identify if a tool is required and then associate one or more tools for each sub-task. This deviates from conventional RAG where an n-best list of tools are identified given the complex task directly. Experiments are presented on the UltraTool benchmark corpus with 1000 complex tasks and over 2000 tools to select from. A conventional RAG-system is established as baseline and compared to the ToolReAGt approach, resulting in an 8.9% improved retrieval accuracy score recall@5.</abstract>
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%0 Conference Proceedings
%T ToolReAGt: Tool Retrieval for LLM-based Complex Task Solution via Retrieval Augmented Generation
%A Braunschweiler, Norbert
%A Doddipatla, Rama
%A Zorila, Tudor-catalin
%Y Zhang, Yuji
%Y Chen, Canyu
%Y Li, Sha
%Y Geva, Mor
%Y Han, Chi
%Y Wang, Xiaozhi
%Y Feng, Shangbin
%Y Gao, Silin
%Y Augenstein, Isabelle
%Y Bansal, Mohit
%Y Li, Manling
%Y Ji, Heng
%S Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-283-1
%F braunschweiler-etal-2025-toolreagt
%X Artificial intelligence agents when deployed to solve complex problems, need to first decompose the task into smaller manageable sub-tasks, and further associate tools if one is required to solve the sub-task. If the size of the set of tools to chose from is large, a retrieval system is usually employed to narrow down the tool choices before the LLM can proceed with associating tools to the sub-tasks. This paper focuses on the retrieval problem to identify the set of relevant tools to solve a complex task given a large pool of tools to chose from using retrieval augmented generation (RAG) and we refer to it as ToolReAGT. The proposed approach employs ReAct prompting to perform the retrieval in an iterative fashion to first identify if a tool is required and then associate one or more tools for each sub-task. This deviates from conventional RAG where an n-best list of tools are identified given the complex task directly. Experiments are presented on the UltraTool benchmark corpus with 1000 complex tasks and over 2000 tools to select from. A conventional RAG-system is established as baseline and compared to the ToolReAGt approach, resulting in an 8.9% improved retrieval accuracy score recall@5.
%R 10.18653/v1/2025.knowllm-1.7
%U https://aclanthology.org/2025.knowllm-1.7/
%U https://doi.org/10.18653/v1/2025.knowllm-1.7
%P 75-83
Markdown (Informal)
[ToolReAGt: Tool Retrieval for LLM-based Complex Task Solution via Retrieval Augmented Generation](https://aclanthology.org/2025.knowllm-1.7/) (Braunschweiler et al., KnowLLM 2025)
ACL