Xiaoyun Mo — accepted AI-conference papers

2025

HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios

Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin +4

ACL 2025finding

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 re…

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2025

Robust Function-Calling for On-Device Language Model via Function Masking

Qiqiang Lin, Muning Wen, Qiuying Peng, Guanyu Nie, Junwei Liao, Jun Wang +5

ICLR 2025spotlight

Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhancements in their function-calling capabilities. This p…

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