People are increasingly turning to large language models (LLMs) for complex information tasks like academic research or planning a move to another city. However, while they often require working in a nonlinear manner - e.g., to arrange information spatially to organize and make sense of it, current interfaces for interacting with LLMs are generally linear to support conversational interaction. To address this limitation and explore how we can support LLM-powered exploration and sensemaking, we developed Sensecape, an interactive system designed to support complex information tasks with an LLM by enabling users to (1) manage the complexity of information through multilevel abstraction and (2) seamlessly switch between foraging and sensemaking. Our within-subject user study reveals that Sensecape empowers users to explore more topics and structure their knowledge hierarchically. We contribute implications for LLM-based workflows and interfaces for information tasks.

翻译：人们越来越依赖大语言模型（LLMs）完成复杂信息任务，例如学术研究或规划迁居。然而，尽管这些任务通常需要非线性工作方式——例如通过空间化信息排列来组织并理解信息——当前与LLM交互的界面普遍采用线性对话模式。为解决这一局限并探索如何支持基于LLM的信息探索与意义构建，我们开发了Sensecape交互系统。该系统通过支持用户（1）通过多层次抽象管理信息复杂度，以及（2）在信息搜寻与意义构建间无缝切换，实现与LLM协同完成复杂信息任务。我们的被试内用户研究表明，Sensecape能帮助用户探索更多主题并分层结构化认知。我们为基于LLM的信息任务工作流与界面设计提供了重要启示。