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.

研究指出，当前交互界面仅限于线性交互，限制了大型语言模型在非线性信息任务中的应用。因此开发了一个名为Sensecape的交互式系统，旨在通过多层次抽象和允许用户在搜索和意义理解之间无缝切换来支持LLM。该研究揭示了Sensecape增强了用户探索更多的主题和分层次结构知识的能力，并且对LLM工作流和信息任务的界面提出了启示。

Sensecape: 利用大型语言模型实现多级探索与感知