LM -Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation (2024)

Abstract: Semi-structured interviews are a crucial method of data acquisition in qualitative research. Typically controlled by the interviewer, the process progresses through a question-and-answer format, aimed at eliciting information from the interviewee. However, interviews are highly time-consuming and demand considerable experience of the interviewers, which greatly limits the efficiency and feasibility of data collection. Therefore, we introduce LM-Interview, a novel system designed to automate the process of preparing, conducting and analyzing semi-structured interviews. Experimental results demonstrate that LM-interview achieves performance comparable to that of skilled human interviewers.

## introduction interviews are a widely employed method that exerts a profound influence in the field of qualitative research. the central concept of structured interviews is to ensure that each interview is conducted with exactly the same questions presented in the same order. this standardization ensures that answers can be reliably aggregated and that comparisons can be confidently made between different subgroups within the sample or across various survey periods. on the basis of structured interviews, semi-structured interviews take a step further by breaking the constraints of a fixed set of questions and predefined order, posing probing questions to the details emerged during the interviews, therefore enabling the uncovering of deeper knowledge and more profound associations while maintaining a similar level of comparability between samples as structured interviews. however, conducting semi-structured interviews necessitates extensive involvement of experienced researchers, which severely limits the efficiency of data collection, hence the generalizability of the researches. presented system. in this paper, we introduce lm-interview, a system designed to support qualitative researchers throughout the procedure of semistructured interviews. by leveraging knowledgeguided language model exploitation, lm-interview addresses each of the three identified gaps through strategically designed modules, the workflow of which aligns with the typical process division for conducting semi-structured interviews described in classical literature @xcite . qualitative researchers can utilize our system to construct the interview guides before interview, then gather extensive data by llm-driven interviews without the need for human labor, and finally, gain insights from the system's analysis of the interview data to advance their researches. contributions. (1) we propose the use of a knowledge-guided language model to automate the process of conducting semi-structured interviews. (2) we implement lm-interview, a comprehensive system designed to supporting qualitative researchers throughout the entire process of designing, conducting, and analysing interviews. (3) we conduct experiments demonstrating that the system achieves a level of performance comparable to that of experienced human interviewers. ## the interview system the typical process @xcite of carrying out an interview is dividing it into three stages: (1) constructing the interview guide before the interview, (2) chatting with the interviewee to gather information during the interview, and (3) after the interview, encoding the discourse and conduct conversation analysis. following this widelyapplied paradigm, we design multiple modules for all the three stages as shown in figure 1 , which are all empowered by language model coordination. ## experiments in this section, we conduct real-scenario experiments to evaluate the proposed system. specifically, we assess the system's ability to (1) conduct and (2) analyze interviews. ## conclusion we introduced lm-interviewer, a system powered by knowledge-guided language model for automating the complete process of semi-structured interviews. with lm-interviewer, qualitative researchers can efficiently collect and preliminarily analyze large volumes of data without the need for extensive human effort. we demonstrated that the system performs at a level comparable to experienced human interviewers in real-world setting. we believe that lm-interviewer will not only serve as a valuable tool but also expand the boundary of qualitative researches.