Wang Xinzhi

2 accepted papers

2025

ACEBench: A Comprehensive Evaluation of LLM Tool Usage

Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu +5

EMNLP 2025

Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation

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2025

ToolACE: Winning the Points of LLM Function Calling

Weiwen Liu, Xu Huang, Xingshan Zeng, xinlong hao, Shuai Yu, Dexun Li +21

ICLR 2025poster

Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pi…

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