MORPHEUS：通过探索和利用潜在空间从个性化对话历史中建模角色
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space
AI总结：
本文提出MORPHEUS框架，通过人物设定码本和角色后验分布在潜在空间中建模角色，以三阶段训练实现无外部角色数据的个性化对话生成，并在中英文数据集上验证其效果。
摘要：
个性化对话生成（Personalized Dialogue Generation，PDG）旨在根据角色或人物设定生成连贯的回复。传统PDG依赖外部角色数据，而这些数据可能稀缺并引发隐私问题。已有方法通过从对话历史中提取角色信息来解决这些问题，但往往无法在连续空间中对角色进行通用建模。为克服这些局限，我们提出了一个新颖框架——通过探索和利用潜在空间从个性化对话历史中建模角色（Models Roles from Personalized Dialogue History by Exploring and Utilizing Latent Space，MORPHEUS），该框架采用三阶段训练流程。具体而言，我们创建了一个人物设定码本，以在潜在空间中紧凑地表示角色，并使用该码本构建角色信息的后验分布。该方法使模型能够跨角色泛化，即使面对未见过的角色也能生成个性化对话。在中文和英文数据集上的实验表明，MORPHEUS增强了角色信息提取，并在没有外部角色数据的情况下改进了回复生成。此外，MORPHEUS可被视为面向大语言模型的一种高效微调方法。
英文摘要：
Personalized Dialogue Generation (PDG) aims to create coherent responses according to roles or personas. Traditional PDG relies on external role data, which can be scarce and raise privacy concerns. Approaches address these issues by extracting role information from dialogue history, which often fail to generically model roles in continuous space. To overcome these limitations, we introduce a novel framework \textbf{MO}dels \textbf{R}oles from \textbf{P}ersonalized Dialogue \textbf{H}istory by \textbf{E}xploring and \textbf{U}tilizing Latent \textbf{S}pace (MORPHEUS) through a three-stage training process. Specifically, we create a persona codebook to represent roles in latent space compactly, and this codebook is used to construct a posterior distribution of role information. This method enables the model to generalize across roles, allowing the generation of personalized dialogues even for unseen roles. Experiments on both Chinese and English datasets demonstrate that MORPHEUS enhances the extraction of role information, and improves response generation without external role data. Additionally, MORPHEUS can be considered an efficient fine-tuning for large language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
