Post Persona Alignment for Multi-Session Dialogue Generation
Journal:
arXiv
Published Date:
Jun 13, 2025
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.
