@inproceedings{zhang-etal-2025-sotopia,
    title = "{SOTOPIA}-: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social Agents",
    author = "Zhang, Wenyuan  and
      Liu, Tianyun  and
      Song, Mengxiao  and
      Li, Xiaodong  and
      Liu, Tingwen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1203/",
    pages = "24669--24697",
    ISBN = "979-8-89176-251-0",
    abstract = "Despite the abundance of prior social strategies possessed by humans, there remains a paucity of research dedicated to their transfer and integration into social agents. Our proposed SOTOPIA-{\ensuremath{\Omega}} framework aims to address and bridge this gap, with a particular focus on enhancing the social capabilities of language agents. This framework dynamically injects a variety of social strategies into expert agents, thereby automating the construction of high-quality social dialogue training corpus. Additionally, we introduce the concept of Social Instruction Following (S-IF) and propose two new S-IF evaluation metrics that are complementary to social capability. We demonstrate that several 7B models trained on high-quality corpus not only significantly surpasses the expert agent (GPT-4) in achieving social goals but also enhances S-IF performance. Analysis and variant experiments validate the advantages of dynamic construction, which can especially break the agent{'}s prolonged deadlock."
}