@inproceedings{mou-etal-2025-agentsense,
    title = "{A}gent{S}ense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios",
    author = "Mou, Xinyi  and
      Liang, Jingcong  and
      Lin, Jiayu  and
      Zhang, Xinnong  and
      Liu, Xiawei  and
      Yang, Shiyue  and
      Ye, Rong  and
      Chen, Lei  and
      Kuang, Haoyu  and
      Huang, Xuanjing  and
      Wei, Zhongyu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.257/",
    pages = "4975--5001",
    ISBN = "979-8-89176-189-6",
    abstract = "Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their capacity to navigate complex social interactions remains a challenge. Previous studies face limitations due to insufficient scenario diversity, complexity, and a single-perspective focus. To this end, we introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios. Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts. We evaluate LLM-driven agents through multi-turn interactions, emphasizing both goal completion and implicit reasoning. We analyze goals using ERG theory and conduct comprehensive experiments. Our findings highlight that LLMs struggle with goals in complex social scenarios, especially high-level growth needs, and even GPT-4o requires improvement in private information reasoning."
}