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

Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Xuan Yan, Jiaxin Yuan, Bin Xu, Juanzi Li, Zhiyuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of System Demonstrations
EditorsDelia Irazu Hernandez Farias, Tom Hope, Manling Li
PublisherAssociation for Computational Linguistics (ACL)
Pages520-528
Number of pages9
ISBN (Electronic)9798891761674
StatePublished - 2024
Externally publishedYes
Event2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 - Hybrid, Miami, United States
Duration: 12 Nov 202416 Nov 2024

Publication series

NameEMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of System Demonstrations

Conference

Conference2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
Country/TerritoryUnited States
CityHybrid, Miami
Period12/11/2416/11/24

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