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, thanks to the externalization of levels of abstraction. We contribute implications for LLM-based workflows and interfaces for information tasks.

翻译：人们越来越多地借助大语言模型处理复杂信息任务，如学术研究或规划迁居至另一城市。然而，这些任务往往需要非线性工作模式——例如通过空间排列信息以组织并理解内容——但当前与大语言模型交互的界面通常采用线性结构以支持对话式交互。为突破这一局限并探索如何赋能大语言模型驱动的探索与意义建构，我们开发了Sensecape交互式系统。该系统通过两大机制支持用户借助大语言模型完成复杂信息任务：（1）通过多层次抽象管理信息复杂度；（2）在信息采集与意义建构间无缝切换。基于受试者内设计的用户研究表明，得益于抽象层次的外显化呈现，Sensecape能够帮助用户探索更多主题并以层级化结构组织知识。本研究为基于大语言模型的信息任务工作流与界面设计提供了重要启示。