先生成通用回复，再用回复反向匹配个性记忆，让对话更自然多样。
Post Persona Alignment for Multi-Session Dialogue Generation
- 先不看个性信息，生成通用回复，再用回复找个性记忆
- 在一致性、多样性、个性相关性上全面优于旧方法
- 适合需要长期个性化对话的场景，如虚拟角色陪伴
多轮对话生成面临长期一致性与个性化表达的挑战。尽管大语言模型在单轮对话中表现优异，但在跨会话场景下难以保持个性一致性和对话连贯性。现有方法通常在生成前检索个性信息，导致回复单调。本文提出后置个性对齐（PPA）框架，采用两阶段策略：首先仅基于对话上下文生成通用回复；随后以该回复为查询，检索相关个性记忆，并对回复进行精细化调整以匹配说话人个性。这种后处理对齐机制在保持个性一致性的同时提升回复自然度与多样性。在多轮对话数据上的实验表明，PPA在一致性、多样性及个性相关性方面显著优于现有方法，为长期个性化对话生成提供了更灵活高效的范式。
原文摘要 · Abstract (English)
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.
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