A Dialogue System for Semi-Structured Interviews by LLM s and its Evaluation on Persona Information Collection (2025)

Abstract: 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.

## introduction this paper proposes a dialogue control management framework using large language models (llms) @xcite , specifically targeting semi-structured interviews @xcite . semi-structured interviews are characterized by the interviewer starting the dialogue with only an initial slot set prepared in advance and dynamically expanding the list of slots throughout the interview's dialogues with all interviewees. in this paper, career interviews @xcite are highlighted as a representative example of a semi-structured interview. in the proposed framework, llms are applied to generate the interviewer's utterances and to make conditional branching decisions based on the understanding of the interviewee's responses. this approach allows for flexible dialogue control by generating and updating slots in response to the interviewee's answers. within this framework, llms function as the agent performing the role of the interviewer, responsible for generating utterances, generating new slots, and filling slots. furthermore, unlike @xcite , the framework incorporates a mechanism for accumulating the list of slots generated over the course of incrementing the number of interviewees during semi-structured interviews. as the key components of the proposed method are to be described in section 5.2 and in figure 4 , we invented the "persona attributes prediction" function, which contributes to prioritizing generating questions known to be relevant to collecting persona attributes and values foot_0 . the contributions of this paper are as follows: in recent years, numerous studies on interview dialogue systems have been reported @xcite @xcite @xcite @xcite , including an interview system designed for job interviews as a notable example @xcite . additionally, @xcite developed a job interview dialogue system that employs an android robot. this system generates follow-up questions based on user response keywords and the degree of satisfaction, guided by four predefined topics. in a different line of work, @xcite presented a framework for statistically modeling the response behavior of a user simulator, with the aim of optimizing dialogue policies. moreover, related to interview dialogue systems, there have been efforts to enhance the performance of dialogue systems by leveraging llms in slot filling dialogue systems @xcite @xcite , utilizing gpt's advanced language comprehension capabilities @xcite @xcite . wagner and ultes (2024) discuss an approach that enhances controllability in dialog systems by integrating a rule or slot based dialog manager alongside llms. furthermore, @xcite proposed a method for dynamically generating slots based on dialogue history to maintain scenario consistency in tabletop role-playing games (trpgs). in trpgs, the primary objective is to record scenarios, and deep exploration of information is not a priority. however, in semi-structured interviews, it is necessary to dynamically generate new slots as needed to obtain new information. additionally, @xcite developed a dialogue system aimed at supporting efficiency and quality improvements in career interviews conducted by nursing administrators. building on the results of @xcite , this paper also focuses on career interviews conducted by nursing administrators, modeling semi-structured interviews that dynamically generate slots according to the interviewee's circumstances. research on semi-structured interviews has also been conducted @xcite . in contrast to these existing studies, this paper proposes a dialogue control framework using llms. the proposed method enables more efficient questioning in semi-structured interviews with multiple interviewees by accumulating information in the form of slots throughout the interview. ## semi-structured interview representative interview formats include structured interviews and semi-structured interviews. these formats differ significantly in whether the list of slots is finalized before the start of the interview. figure 1 illustrates conceptual diagrams of these two interviews types. a semi-structured interview is characterized by the fact that, although basic questions are prepared in advance, the interviewer can flexibly change their order of the questions or add new questions according to the interviewee's responses (figure 1 , right). the interviewer progressively constructs the list of slots through dialogue with all interviewees. this format allows a higher degree of freedom in questioning and enables in-depth exploration tailored to each interviewee and situation. as a result, it can uncover the list of slots that might not emerge in a structured interview. however, the outcome can vary significantly depending on the interviewer's skill level, requiring more advanced interview techniques and experience. ## flowchart for semi-structured interviews in this paper, we use a flowchart-based approach to control the progress of dialogue in semi-structured interviews. figure 2 shows the flowcharts designed for semi-structured interviews. figure 3 illustrates the internal module common to structured and semi-structured interviews, which is used in both models. ## conclusion this paper proposed a dialogue control management framework using llms for semi-structured interviews, with a particular focus on career interviews conducted by nursing administrators. the proposed framework enables flexible dialogue control by dynamically generating and updating slots based on the interviewee's responses, as well as progressively constructing the list of slots throughout interviews with multiple interviewees. we demonstrated that llms can effectively perform the role of interviewer, facilitating semi-structured interviews through generating slots, filling slots, generating question, and conditional branching decisions. by implementing a mechanism for accumulating the list of generated slots, the proposed framework achieved more efficient and precise question generation across a series of interviews. evaluation results confirmed that the proposed approach outperforms the baseline without slot accumulation in terms of the number of persona attributes and values collected throughout the semi-structured interviews. these results demonstrate the effectiveness of the proposed framework for the difficult task of dialogue control management in semi-structured interviews. to address the current limitations, future work will include experiments using larger and more diverse datasets that better reflect real world scenarios, experiments involving actual human participants as interviewees, the adoption of more persuasive evaluation methods, further differentiation between our approach and existing slot-filling dialogue systems, comparisons with other dialogue management approaches, evaluations using multiple llms beyond gpt-4o, the expansion of both the number and diversity of personas, and an investigation into issues that arise from long-term slot accumulation and possible solutions.