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.

通过在对话历史中提取角色信息并构建潜在空间中的角色代码书，MORPHEUS 框架通过三阶段训练过程使模型能够泛化到不同角色，从而实现个性化对话生成，无需外部角色数据，并且可以作为大型语言模型的高效微调。