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    <abstract>Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized responses. While large language models (LLMs) excel in single-session dialogues, they struggle to preserve persona fidelity and conversational coherence across extended interactions. Existing methods typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. We propose Post Persona Alignment (PPA), a novel two-stage framework that reverses this process. PPA first generates a general response based solely on dialogue context, then retrieves relevant persona memories using the response as a query, and finally refines the response to align with the speaker’s persona. This post-hoc alignment strategy promotes naturalness and diversity while preserving consistency and personalization. Experiments on multi-session LLM-generated dialogue data demonstrate that PPA significantly outperforms prior approaches in consistency, diversity, and persona relevance, offering a more flexible and effective paradigm for long-term personalized dialogue generation.</abstract>
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%0 Conference Proceedings
%T Post Persona Alignment for Multi-Session Dialogue Generation
%A Chen, Yi-Pei
%A Nishida, Noriki
%A Nakayama, Hideki
%A Matsumoto, Yuji
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chen-etal-2025-post
%X Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized responses. While large language models (LLMs) excel in single-session dialogues, they struggle to preserve persona fidelity and conversational coherence across extended interactions. Existing methods typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. We propose Post Persona Alignment (PPA), a novel two-stage framework that reverses this process. PPA first generates a general response based solely on dialogue context, then retrieves relevant persona memories using the response as a query, and finally refines the response to align with the speaker’s persona. This post-hoc alignment strategy promotes naturalness and diversity while preserving consistency and personalization. Experiments on multi-session LLM-generated dialogue data demonstrate that PPA significantly outperforms prior approaches in consistency, diversity, and persona relevance, offering a more flexible and effective paradigm for long-term personalized dialogue generation.
%U https://aclanthology.org/2025.findings-emnlp.1098/
%P 20184-20192

Markdown (Informal)

[Post Persona Alignment for Multi-Session Dialogue Generation](https://aclanthology.org/2025.findings-emnlp.1098/) (Chen et al., Findings 2025)

• Post Persona Alignment for Multi-Session Dialogue Generation (Chen et al., Findings 2025)

ACL

• Yi-Pei Chen, Noriki Nishida, Hideki Nakayama, and Yuji Matsumoto. 2025. Post Persona Alignment for Multi-Session Dialogue Generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20184–20192, Suzhou, China. Association for Computational Linguistics.