With the rapid growth of scholarly archives, researchers subscribe to "paper
alert" systems that periodically provide them with recommendations of recently
published papers that are similar to previously collected papers. However,
researchers sometimes struggle to make sense of nuanced connections between
recommended papers and their own research context, as existing systems only
present paper titles and abstracts. To help researchers spot these connections,
we present PaperWeaver, an enriched paper alerts system that provides
contextualized text descriptions of recommended papers based on user-collected
papers. PaperWeaver employs a computational method based on Large Language
Models (LLMs) to infer users' research interests from their collected papers,
extract context-specific aspects of papers, and compare recommended and
collected papers on these aspects. Our user study (N=15) showed that
participants using PaperWeaver were able to better understand the relevance of
recommended papers and triage them more confidently when compared to a baseline
that presented the related work sections from recommended papers.

研究人员订阅 “论文提醒” 系统以获取最近发布的与其之前收集的论文相似的推荐论文，但由于现有系统仅提供论文标题和摘要，研究人员有时难以理解这些推荐论文与其自身研究背景之间微妙的联系。为了帮助研究人员发现这些联系，我们提出了基于用户收集的论文给推荐论文提供背景化文本描述的 PaperWeaver 系统。PaperWeaver 采用基于大型语言模型（LLMs）的计算方法来推断用户的研究兴趣，提取论文的具体上下文方面，并在这些方面上比较推荐与收集的论文。我们的用户研究结果（N=15）表明，使用 PaperWeaver 的参与者能够更好地理解推荐论文的相关性，并在与基线系统（基于推荐论文的相关工作部分）相比时更自信地进行分流。