Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists

Yue Cui, Liuyi Yao, Shuchang Tao, Weijie Shi, Yaliang Li, Bolin Ding, Xiaofang Zhou


Abstract
Large language models (LLMs) have significantly advanced natural language processing, particularly through the integration of external tools and APIs. However, their effectiveness is frequently hampered by parameter mis-filling during tool calling. In this paper, we propose the Hierarchical Tool Error Checklist (HiTEC) framework to systematically diagnose and mitigate tool-calling errors without relying on extensive real-world interactions. HiTEC introduces a two-tiered approach: a global error checklist that identifies common, cross-tool issues, and a local error checklist that targets tool-specific and contextual failures. Building on this structure, we propose two deployments: HiTEC-In Context Learning (HiTEC-ICL) and HiTEC-Kahneman-Tversky Optimization (HiTEC-KTO). HiTEC-ICL embeds the global checklist in the initial prompts and leverages a two-round conversational interaction to dynamically refine parameter handling, while HiTEC-KTO generates high-quality negative examples to drive fine-tuning via preference-based optimization. Extensive experiments across five public datasets demonstrate that our framework significantly improves parameter-filling accuracy and tool-calling success rates compared to baseline methods.
Anthology ID:
2025.findings-acl.841
Volume:
Findings of the Association for Computational Linguistics: ACL 2025
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
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Pages:
16357–16375
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URL:
https://preview.aclanthology.org/display_plenaries/2025.findings-acl.841/
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Cite (ACL):
Yue Cui, Liuyi Yao, Shuchang Tao, Weijie Shi, Yaliang Li, Bolin Ding, and Xiaofang Zhou. 2025. Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16357–16375, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists (Cui et al., Findings 2025)
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https://preview.aclanthology.org/display_plenaries/2025.findings-acl.841.pdf