@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
      Jincheng, Jincheng  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://preview.aclanthology.org/corrections-2025-08/2025.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."
}