@inproceedings{wei-etal-2025-mecot,
    title = "{MEC}o{T}: {M}arkov Emotional Chain-of-Thought for Personality-Consistent Role-Playing",
    author = "Wei, Yangbo  and
      Huang, Zhen  and
      Zhao, Fangzhou  and
      Feng, Qi  and
      Xing, Wei W.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.435/",
    pages = "8297--8314",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have shown remarkable capabilities in role-playing dialogues, yet they often struggle to maintain emotionally consistent and psychologically plausible character personalities. We present MECoT (Markov Emotional Chain-of-Thought), a framework that enhances LLMs' ability to generate authentic personality-driven dialogues through stochastic emotional transitions. Inspired by dual-process theory, MECoT combines a Markov-chain-driven emotional processor for intuitive responses with an LLM-based reasoning mechanism for rational regulation, mapped onto a 12-dimensional Emotion Circumplex Model. The framework dynamically adjusts emotional transitions using personality-weighted matrices and historical context, ensuring both emotional coherence and character consistency. We introduce the Role-playing And Personality Dialogue (RAPD) dataset, featuring diverse character interactions with fine-grained emotional annotations, along with novel metrics for evaluating emotional authenticity and personality alignment. Experimental results demonstrate MECoT{'}s effectiveness, achieving 93.3{\%} emotional accuracy on RAPD and substantially outperforming existing approaches. Our analysis reveals optimal emotional granularity (12-16 categories) and validates our data-driven personality optimization approach. Code and data are available at \url{https://anonymous.4open.science/r/MECoT}"
}