关键词: 回忆疗法, 多轮对话生成, 角色扮演, 智能养老, 语料库构建, 人机交互

Abstract: Since existing dialogue generation technologies in reminiscence therapy suffer from rigid interactions and poor guidance due to the lack of high-quality domain-specific corpora, a Scenario-driven Character Information Generation and Role-playing (SCIGR) approach was proposed to generate character information and construct multi-turn role-playing dialogue data. First, detailed image descriptions were produced to create reminiscence scenarios. Then, retrieval-augmented generation techniques and prompt engineering were integrated to develop character information that aligns with the life backgrounds of elderly individuals. Subsequently, photo-shooting stories were constructed to enrich the dialogue context. Finally, multi-turn dialogues were generated through role-playing based on the above information, resulting in the establishment of the first Chinese multi-turn dialogue dataset for image-based reminiscence therapy, named IRTDialogues. To verify the effectiveness of this dataset, a Multimodal Large Language Model (MLLM) was trained using Supervised Fine-Tuning (SFT), and a Multi-dimensional Multimodal Reminiscence Therapy benchmark (MMRT) covering five dimensions—including empathy and guidance—was developed for evaluation. Experimental results demonstrate that models train on the IRTDialogues dataset are capable of generating responses that are more contextually appropriate and emotionally supportive. This provides both data support and a new implementation pathway for developing intelligent elderly care technologies with greater humanistic concern.

Key words: reminiscence therapy, multi-turn dialogue generation, role-playing, intelligent elderly care, corpus construction, human-computer interaction