标题： 从关键词到语义：数据发现中大型语言模型的感知 显示英文标题

标题： From keywords to semantics: Perceptions of large language models in data discovery

摘要： 当前的数据发现方法在元数据和查询之间匹配关键词。 这种匹配要求研究人员知道其他研究人员之前使用的准确用语，这导致了一个具有挑战性的过程，可能导致遗漏相关数据。 大型语言模型（LLMs）可以通过消除这一要求，使研究人员能够用自然语言提问，从而增强数据发现。 然而，我们目前还不知道研究人员是否会接受使用LLMs进行数据发现。 在以人为本的人工智能（HCAI）的焦点下，我们进行了焦点小组讨论（N = 27），以了解研究人员对LLMs用于数据发现的看法。 我们的概念模型显示，潜在的好处不足以让研究人员用LLMs取代现有技术。 障碍阻止研究人员完全接受LLMs，但围绕透明度的功能可能克服这些障碍。 使用我们的模型将使开发者能够融入导致LLMs在数据发现中被更多接受的功能。 显示英文摘要

摘要： Current approaches to data discovery match keywords between metadata and queries. This matching requires researchers to know the exact wording that other researchers previously used, creating a challenging process that could lead to missing relevant data. Large Language Models (LLMs) could enhance data discovery by removing this requirement and allowing researchers to ask questions with natural language. However, we do not currently know if researchers would accept LLMs for data discovery. Using a human-centered artificial intelligence (HCAI) focus, we ran focus groups (N = 27) to understand researchers' perspectives towards LLMs for data discovery. Our conceptual model shows that the potential benefits are not enough for researchers to use LLMs instead of current technology. Barriers prevent researchers from fully accepting LLMs, but features around transparency could overcome them. Using our model will allow developers to incorporate features that result in an increased acceptance of LLMs for data discovery.