DiscipLink：通过人机协同探索展开跨学科信息寻求过程
DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration
AI总结：
本文提出人机协同交互系统DiscipLink，利用大语言模型从多学科视角生成探索性问题并辅助文献检索与筛选，帮助研究者打破学科边界、整合分散知识，并通过实验验证其有效性。
摘要：
跨学科研究通常要求研究者在不同知识分支中探索文献。然而，在不熟悉的学科中浏览高度分散的知识构成了重大挑战。本文介绍了DiscipLink，这是一个新颖的交互式系统，旨在促进研究者与大型语言模型（LLMs）在跨学科信息寻求（IIS）中的协作。基于用户感兴趣的主题，DiscipLink从可能相关的学科领域视角发起探索性问题，用户可以进一步调整这些问题。随后，DiscipLink通过自动扩展带有学科特定术语的查询、从检索到的论文中提取主题，并突出论文与问题之间的联系，支持用户在选定问题下搜索和筛选论文。我们的评估包括一项受试者内比较实验和一项开放式探索性研究，结果表明DiscipLink能够有效支持研究者打破学科边界并整合不同领域的分散知识。研究结果强调了由LLM驱动的工具在促进信息寻求实践和加强跨学科研究方面的潜力。
英文摘要：
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' topics 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
