@inproceedings{gao-etal-2025-tailorrpa,
    title = "{T}ailor{RPA}: A Retrieval-Based Framework for Eliciting Personalized and Coherent Role-Playing Agents in General Domain",
    author = "Gao, Zhenpeng  and
      Xing, Xiaofen  and
      Xu, Xiangmin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.288/",
    doi = "10.18653/v1/2025.findings-emnlp.288",
    pages = "5381--5412",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent advancements of general domain oriented Role-playing Agents (RPAs) have enabled the agents to maintain character properties in a wide spectrum of daily tasks beyond mere scenario based chit-chatting. Nonetheless, current works lacks consideration of replicating internal properties of characters like fine-grained memories, and failed to take account of aligning with the knowledge boundary of each character, resulting in degraded personalization and proneness to character hallucination in general domain. To address these problems, we draw inspirations from the context effect theory and propose a retrieval-based framework TailorRPA to harvest tailored general domain instructions to improve integration of fine-grained memories and incorporate general-domain protective queries to help shape the character-wise knowledge boundary, alleviating character hallucination. Based on the framework, we developed a role-playing dataset TailorGen, comprising both role-specific and general-domain instructions. Through empirical experiments, we proved the superiority of TailorRPA in eliciting general domain role-playing capabilities and alleviating character hallucination compared to baseline methods, and explored the existence of character hallucination in state-of-the-art proprietary models through empirical experiments, underlining the importance of our work."
}