  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.
