Decision-making 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 seeing an overview of the information space upfront, such as the criteria that others have previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- it not only requires significant input from previous users to generate and share these overviews, but such overviews may also 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：在陌生领域的决策往往充满挑战，用户需要投入大量精力从不同维度比较各种选项。先前研究与我们的形成性研究发现，人们若能预先了解信息空间的概貌（例如先前他人认为具有参考价值的评估标准），将能从中获益。然而，现有意义建构工具面临“冷启动”困境——不仅需要大量用户贡献才能生成并共享这些概览，且此类概览可能存在偏差与不完整性。本研究提出新型系统Selenite，通过将大型语言模型（LLMs）作为推理引擎与知识检索器，自动生成涵盖选项与评估标准的综合概览，从而启动用户的意义建构过程。在此基础上，Selenite还能随用户使用过程动态调整，以系统化且个性化的方式帮助用户发现、阅读和导航陌生信息。通过三项实验验证，我们发现Selenite能够可靠生成准确且高质量的概览，显著加速用户信息处理效率，并有效提升其整体理解能力与意义建构体验。