@inproceedings{wang-etal-2025-hammerbench,
title = "{H}ammer{B}ench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios",
author = "Wang, Jun and
Zhou, Jiamu and
Wang, Xihuai and
Mo, Xiaoyun and
Zhang, Haoyu and
Lin, Qiqiang and
Jin, Cheng and
Wen, Muning and
Zhang, Weinan and
Peng, Qiuying and
Wang, Jun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.175/",
doi = "10.18653/v1/2025.findings-acl.175",
pages = "3350--3376",
ISBN = "979-8-89176-256-5",
abstract = "Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In this paper, we introduce HammerBench, a novel benchmark framework for assessing LLMs' function-calling capabilities in real-world, multi-turn dialogues. HammerBench simulates diverse mobile assistant use cases, incorporating imperfect instructions, dynamic question-answer trajectories, intent and argument shifts, and the indirect use of external information through pronouns. To construct this benchmark, we curate a comprehensive dataset derived from popular mobile app functionalities and anonymized user logs, complemented by a cost-effective data generation pipeline leveraging open-source models. HammerBench is further augmented with fine-grained interaction snapshots and metrics, enabling detailed evaluation of function-calling performance across individual conversational turns. We demonstrate the effectiveness of HammerBench by evaluating several leading LLMs and uncovering key performance trends. Our experiments reveal that different types of parameter name errors are a significant source of failure across different interaction scenarios, highlighting critical areas for further improvement in LLM robustness for mobile assistant applications."
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%0 Conference Proceedings
%T HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios
%A Wang, Jun
%A Zhou, Jiamu
%A Wang, Xihuai
%A Mo, Xiaoyun
%A Zhang, Haoyu
%A Lin, Qiqiang
%A Jin, Cheng
%A Wen, Muning
%A Zhang, Weinan
%A Peng, Qiuying
%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 wang-etal-2025-hammerbench
%X Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In this paper, we introduce HammerBench, a novel benchmark framework for assessing LLMs’ function-calling capabilities in real-world, multi-turn dialogues. HammerBench simulates diverse mobile assistant use cases, incorporating imperfect instructions, dynamic question-answer trajectories, intent and argument shifts, and the indirect use of external information through pronouns. To construct this benchmark, we curate a comprehensive dataset derived from popular mobile app functionalities and anonymized user logs, complemented by a cost-effective data generation pipeline leveraging open-source models. HammerBench is further augmented with fine-grained interaction snapshots and metrics, enabling detailed evaluation of function-calling performance across individual conversational turns. We demonstrate the effectiveness of HammerBench by evaluating several leading LLMs and uncovering key performance trends. Our experiments reveal that different types of parameter name errors are a significant source of failure across different interaction scenarios, highlighting critical areas for further improvement in LLM robustness for mobile assistant applications.
%R 10.18653/v1/2025.findings-acl.175
%U https://aclanthology.org/2025.findings-acl.175/
%U https://doi.org/10.18653/v1/2025.findings-acl.175
%P 3350-3376
Markdown (Informal)
[HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios](https://aclanthology.org/2025.findings-acl.175/) (Wang et al., Findings 2025)
ACL
- Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Cheng Jin, Muning Wen, Weinan Zhang, Qiuying Peng, and Jun Wang. 2025. HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3350–3376, Vienna, Austria. Association for Computational Linguistics.