Post Persona Alignment for Multi-Session Dialogue Generation
This addresses the challenge of preserving persona fidelity and conversational coherence in extended interactions for dialogue systems, offering a more flexible paradigm, though it appears incremental as it builds on existing retrieval-based methods by reversing the process.
The paper tackled the problem of maintaining long-term consistency and generating diverse, personalized responses in multi-session persona-based dialogue generation by proposing Post Persona Alignment (PPA), a two-stage framework that first generates a general response and then refines it with persona alignment, resulting in significant improvements in consistency, diversity, and persona relevance over prior methods.
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.