Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- not only requiring significant input from previous users to generate and share these overviews, but also that such overviews may turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages LLMs as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.

翻译：在陌生领域中进行意义建构颇具挑战性，用户需要耗费大量精力根据各种标准比较不同选项。先前研究及我们的形成性研究发现，提前阅读信息空间的概览（包括前人发现有用的标准）对用户大有裨益。然而，现有意义建构工具面临"冷启动"问题——不仅需要大量先前用户输入来生成和共享这些概览，而且此类概览可能带有偏见且不完整。本研究提出新型系统Selenite，将大型语言模型（LLM）作为推理机器和知识检索器，自动生成选项与标准的全面概览，以启动用户的意义建构过程。随后，Selenite会在用户使用过程中自适应调整，帮助用户以系统化且个性化的方式查找、阅读和导航陌生信息。通过三项实验，我们发现Selenite能够可靠地生成准确且高质量的概览，显著加速用户的信息处理过程，并有效提升其整体理解与意义建构体验。