@inproceedings{zeng-etal-2025-bragging,
    title = "It{'}s Not Bragging If You Can Back It Up: Can {LLM}s Understand Braggings?",
    author = "Zeng, Jingjie  and
      Li, Huayang  and
      Yang, Liang  and
      Sun, Yuanyuan  and
      Lin, Hongfei",
    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.858/",
    pages = "17542--17560",
    ISBN = "979-8-89176-251-0",
    abstract = "Bragging, as a pervasive social-linguistic phenomenon, reflects complex human interaction patterns. However, the understanding and generation of appropriate bragging behavior in large language models (LLMs) remains underexplored. In this paper, we propose a comprehensive study that combines analytical and controllable approaches to examine bragging in LLMs. We design three tasks, \textit{bragging recognition, bragging explanation, and bragging generation}, along with novel evaluation metrics to assess the models' ability to identify bragging intent, social appropriateness, and account for context sensitivity. Our analysis reveals the challenges of bragging in the social context, such as recognizing bragging and responding appropriately with bragging in conversation. This work provides new insights into how LLMs process bragging and highlights the need for more research on generating contextually appropriate behavior in LLMs."
}