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    <abstract>Tool learning has emerged as a promising direction by extending Large Language Models’ (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on the general-purpose tool-use capability, which addresses explicit user requirements in instructions. However, they overlook the importance of personalized tool-use capability, leading to an inability to handle implicit user preferences. To address the limitation, we first formulate the task of personalized tool learning, which integrates user’s interaction history towards personalized tool usage. To fill the gap of missing benchmarks, we construct PEToolBench, featuring diverse user preferences reflected in interaction history under three distinct personalized settings, and encompassing a wide range of tool-use scenarios. Moreover, we propose a framework PEToolLLaMA to adapt LLMs to the personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization. Extensive experiments on PEToolBench demonstrate the superiority of PEToolLLaMA over existing LLMs. We release code and data at https://github.com/travis-xu/PEToolBench.</abstract>
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%0 Conference Proceedings
%T PEToolLLM: Towards Personalized Tool Learning in Large Language Models
%A Xu, Qiancheng
%A Li, Yongqi
%A Xia, Heming
%A Liu, Fan
%A Yang, Min
%A Li, Wenjie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F xu-etal-2025-petoolllm
%X Tool learning has emerged as a promising direction by extending Large Language Models’ (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on the general-purpose tool-use capability, which addresses explicit user requirements in instructions. However, they overlook the importance of personalized tool-use capability, leading to an inability to handle implicit user preferences. To address the limitation, we first formulate the task of personalized tool learning, which integrates user’s interaction history towards personalized tool usage. To fill the gap of missing benchmarks, we construct PEToolBench, featuring diverse user preferences reflected in interaction history under three distinct personalized settings, and encompassing a wide range of tool-use scenarios. Moreover, we propose a framework PEToolLLaMA to adapt LLMs to the personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization. Extensive experiments on PEToolBench demonstrate the superiority of PEToolLLaMA over existing LLMs. We release code and data at https://github.com/travis-xu/PEToolBench.
%R 10.18653/v1/2025.findings-acl.1107
%U https://aclanthology.org/2025.findings-acl.1107/
%U https://doi.org/10.18653/v1/2025.findings-acl.1107
%P 21488-21503

Markdown (Informal)

[PEToolLLM: Towards Personalized Tool Learning in Large Language Models](https://aclanthology.org/2025.findings-acl.1107/) (Xu et al., Findings 2025)

• PEToolLLM: Towards Personalized Tool Learning in Large Language Models (Xu et al., Findings 2025)

ACL

• Qiancheng Xu, Yongqi Li, Heming Xia, Fan Liu, Min Yang, and Wenjie Li. 2025. PEToolLLM: Towards Personalized Tool Learning in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21488–21503, Vienna, Austria. Association for Computational Linguistics.