LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation
Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Yan Xuan, Jiaxi Yuan, Bin Xu, Juanzi Li
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.
BibTeX
@inproceedings{li-etal-2024-lm,
title = "{LM}-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation",
author = "Li, Hanming and
Yu, Jifan and
Li, Ruimiao and
Hao, Zhanxin and
Xuan, Yan and
Yuan, Jiaxi and
Xu, Bin and
Li, Juanzi and
Liu, Zhiyuan",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.52/",
doi = "10.18653/v1/2024.emnlp-demo.52",
pages = "520--528"
}