In this paper, we propose a dialogue control management framework using large language models for semi-structured interviews. Specifically, large language models are used to generate the interviewer’s utterances and to make conditional branching decisions based on the understanding of the interviewee’s responses. The framework enables flexible dialogue control in interview conversations by generating and updating slots and values according to interviewee answers. More importantly, we invented through LLMs’ prompt tuning the framework of accumulating the list of slots generated along the course of incrementing the number of interviewees through the semi-structured interviews. Evaluation results showed that the proposed approach of accumulating the list of generated slots throughout the semi-structured interviews outperform the baseline without accumulating generated slots in terms of the number of persona attributes and values collected through the semi-structured interview.