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    <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 https://anonymous.4open.science/r/MECoT</abstract>
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%0 Conference Proceedings
%T MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing
%A Wei, Yangbo
%A Huang, Zhen
%A Zhao, Fangzhou
%A Feng, Qi
%A Xing, Wei W.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wei-etal-2025-mecot
%X 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 https://anonymous.4open.science/r/MECoT
%R 10.18653/v1/2025.findings-acl.435
%U https://aclanthology.org/2025.findings-acl.435/
%U https://doi.org/10.18653/v1/2025.findings-acl.435
%P 8297-8314

Markdown (Informal)

[MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing](https://aclanthology.org/2025.findings-acl.435/) (Wei et al., Findings 2025)

• MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing (Wei et al., Findings 2025)

ACL

• Yangbo Wei, Zhen Huang, Fangzhou Zhao, Qi Feng, and Wei W. Xing. 2025. MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing. In Findings of the Association for Computational Linguistics: ACL 2025, pages 8297–8314, Vienna, Austria. Association for Computational Linguistics.