让对话模型真正理解人物设定，生成更贴合角色的回复。
Persona-Aware Alignment Framework for Personalized Dialogue Generation
- 将人物一致性作为训练目标，直接优化对话与人物设定的匹配度。
- 在多个数据集上超越主流模型和大语言模型，提升角色相关性。
- 适合需要角色化对话的场景，如虚拟助手、游戏NPC等。
个性化对话生成旨在利用人物档案和对话历史生成符合人物特征且连贯的回复。主流模型通常依赖基于标记级语言模型训练（如下一步词预测）来隐式实现个性化，导致模型忽略给定的人物设定，生成通用性回复。为此，我们提出一种新型人物感知对齐框架（PAL），将人物对齐直接设为对话生成的训练目标。PAL采用两阶段训练：人物感知学习与人物对齐，并配备简洁的推理策略‘先选择后生成’，以提升对人物特征的敏感度，在语义层面生成更符合人物特征的回复。大量实验表明，该框架在多个数据集上优于众多先进个性化对话方法及大语言模型。
原文摘要 · Abstract (English)
Personalized dialogue generation aims to leverage persona profiles and dialogue history to generate persona-relevant and consistent responses. Mainstream models typically rely on token-level language model training with persona dialogue data, such as Next Token Prediction, to implicitly achieve personalization, making these methods tend to neglect the given personas and generate generic responses. To address this issue, we propose a novel Persona-Aware Alignment Framework (PAL), which directly treats persona alignment as the training objective of dialogue generation. Specifically, PAL employs a two-stage training method including Persona-aware Learning and Persona Alignment, equipped with an easy-to-use inference strategy Select then Generate, to improve persona sensitivity and generate more persona-relevant responses at the semantics level. Through extensive experiments, we demonstrate that our framework outperforms many state-of-the-art personalized dialogue methods and large language models.
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