Semi-Structured Interview System Based on Fine-Tuned Large Language Model and Reinforcement Learning from Human Feedback

Semi-structured interview is an important method in human-computer interaction research. However, traditional methods often rely on the experience and skills of the interviewer, limiting the quality and flexibility of interview outline. We propose a semi-structured interview system based on fine-tuned large language model (LLM) and reinforcement learning from human feedback (RLHF). Our system uses knowledge in the domain of human-computer interaction to fine-tune LLM and adopts the RLHF method based on multi-task learning and entropy regularization to dynamically adjust interview strategies. The system also collects multimodal data such as speech, video, and emotion to provide comprehensive support for subsequent analysis. Simulation experiment and pilot experiment show that compared to traditional human interview and other baseline methods, our system performs well in terms of interview relevance, personalization, engagement, and efficiency. Qualitative analysis further reveals the system's advantages in terms of conversational fluency, personalization, and efficiency. This innovative interview method is expected to play an important role in future user research, providing researchers with a more efficient and comprehensive data collection tool.