DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration

要旨

Interdisciplinary studies often require researchers to explore literature in diverse branches of knowledge. Yet, navigating through the highly scattered knowledge from unfamiliar disciplines poses a significant challenge. In this paper, we introduce DiscipLink, a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) in interdisciplinary information seeking (IIS). Based on users' topic of interest, DiscipLink initiates exploratory questions from the perspectives of possible relevant fields of study, and users can further tailor these questions. DiscipLink then supports users in searching and screening papers under selected questions by automatically expanding queries with disciplinary-specific terminologies, extracting themes from retrieved papers, and highlighting the connections between papers and questions. Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that DiscipLink can effectively support researchers in breaking down disciplinary boundaries and integrating scattered knowledge in diverse fields. The findings underscore the potential of LLM-powered tools in fostering information-seeking practices and bolstering interdisciplinary research.

著者
Chengbo Zheng
Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Yuanhao Zhang
Hong Kong University of Science and Technology, Hong Kong, China
Zeyu Huang
The Hong Kong University of Science and Technology, New Territories, Hong Kong
Chuhan Shi
Southeast University, Nanjing, China
Minrui Xu
HKUST, Hong Kong, China
Xiaojuan Ma
Hong Kong University of Science and Technology, Hong Kong, Hong Kong
論文URL

https://doi.org/10.1145/3654777.3676366

動画

会議: UIST 2024

ACM Symposium on User Interface Software and Technology

セッション: 3. AI as Copilot

Westin: Allegheny 3
6 件の発表
2024-10-16 01:10:00
2024-10-16 02:40:00