@inproceedings{qin-etal-2025-meta,
title = "Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models",
author = "Qin, Shengqian and
Zhu, Yakun and
Mu, Linjie and
Zhang, Shaoting and
Zhang, Xiaofan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1481/",
doi = "10.18653/v1/2025.acl-long.1481",
pages = "30653--30677",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) have showcased remarkable capabilities as autonomous agents when augmented with external tools. Equipped with fixed tool sets, LLMs struggle with addressing diverse user inquiries in open-world tasks. To evaluate and boost the performance of LLMs in dealing with complex demands in the real-world, we propose open-world function calling, where LLMs need to retrieve suitable tools from a pre-defined external tool library and use retrieved tools to resolve the user{'}s problem. We introduce Meta-Tool, a versatile and plug-and-play tool retrieval system as the access of LLMs to external tool library. Drawing inspiration from the myriad of enhanced approaches associated with Retrieval-Augmented Generation (RAG), Meta-Tool employs a \textit{hypothesize-retrieve-invoke} framework. We further propose Meta-Bench, a comprehensive benchmark for evaluating LLMs in open-world function calling and associated tasks. Meta-Bench encompasses 2,800 dialogues and 7,361 tools, spanning ten distinct scenarios to provide robust and diverse test categories. In conjunction, we present MT-LLaMA, a finetuned version of LLaMA-3.1, which exhibits remarkable performance improvements. Our empirical experiments reveal that Meta-Tool significantly enhances the ability of advanced LLMs to retrieve and leverage the most suitable tools compared to previous tool retrieval methods. Moreover, our fine-tuning enables even smaller-sized LLMs to achieve comparable even exceeding results to GPT-4o. Both the benchmark and the model are made publicly available at https://github.com/qinshengqian/Meta-Tool to foster further research and development in the field."
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<abstract>Large language models (LLMs) have showcased remarkable capabilities as autonomous agents when augmented with external tools. Equipped with fixed tool sets, LLMs struggle with addressing diverse user inquiries in open-world tasks. To evaluate and boost the performance of LLMs in dealing with complex demands in the real-world, we propose open-world function calling, where LLMs need to retrieve suitable tools from a pre-defined external tool library and use retrieved tools to resolve the user’s problem. We introduce Meta-Tool, a versatile and plug-and-play tool retrieval system as the access of LLMs to external tool library. Drawing inspiration from the myriad of enhanced approaches associated with Retrieval-Augmented Generation (RAG), Meta-Tool employs a hypothesize-retrieve-invoke framework. We further propose Meta-Bench, a comprehensive benchmark for evaluating LLMs in open-world function calling and associated tasks. Meta-Bench encompasses 2,800 dialogues and 7,361 tools, spanning ten distinct scenarios to provide robust and diverse test categories. In conjunction, we present MT-LLaMA, a finetuned version of LLaMA-3.1, which exhibits remarkable performance improvements. Our empirical experiments reveal that Meta-Tool significantly enhances the ability of advanced LLMs to retrieve and leverage the most suitable tools compared to previous tool retrieval methods. Moreover, our fine-tuning enables even smaller-sized LLMs to achieve comparable even exceeding results to GPT-4o. Both the benchmark and the model are made publicly available at https://github.com/qinshengqian/Meta-Tool to foster further research and development in the field.</abstract>
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%0 Conference Proceedings
%T Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models
%A Qin, Shengqian
%A Zhu, Yakun
%A Mu, Linjie
%A Zhang, Shaoting
%A Zhang, Xiaofan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F qin-etal-2025-meta
%X Large language models (LLMs) have showcased remarkable capabilities as autonomous agents when augmented with external tools. Equipped with fixed tool sets, LLMs struggle with addressing diverse user inquiries in open-world tasks. To evaluate and boost the performance of LLMs in dealing with complex demands in the real-world, we propose open-world function calling, where LLMs need to retrieve suitable tools from a pre-defined external tool library and use retrieved tools to resolve the user’s problem. We introduce Meta-Tool, a versatile and plug-and-play tool retrieval system as the access of LLMs to external tool library. Drawing inspiration from the myriad of enhanced approaches associated with Retrieval-Augmented Generation (RAG), Meta-Tool employs a hypothesize-retrieve-invoke framework. We further propose Meta-Bench, a comprehensive benchmark for evaluating LLMs in open-world function calling and associated tasks. Meta-Bench encompasses 2,800 dialogues and 7,361 tools, spanning ten distinct scenarios to provide robust and diverse test categories. In conjunction, we present MT-LLaMA, a finetuned version of LLaMA-3.1, which exhibits remarkable performance improvements. Our empirical experiments reveal that Meta-Tool significantly enhances the ability of advanced LLMs to retrieve and leverage the most suitable tools compared to previous tool retrieval methods. Moreover, our fine-tuning enables even smaller-sized LLMs to achieve comparable even exceeding results to GPT-4o. Both the benchmark and the model are made publicly available at https://github.com/qinshengqian/Meta-Tool to foster further research and development in the field.
%R 10.18653/v1/2025.acl-long.1481
%U https://aclanthology.org/2025.acl-long.1481/
%U https://doi.org/10.18653/v1/2025.acl-long.1481
%P 30653-30677
Markdown (Informal)
[Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models](https://aclanthology.org/2025.acl-long.1481/) (Qin et al., ACL 2025)
ACL