Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models
Open accessAbstract
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) switch seamlessly 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.
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Suh et al. highlighted that the current interaction paradigm with LLMs tends to converge on a limited set of ideas, potentially stifling creative exploration.
“Suh et al. [58,59] highlighted that the current interaction paradigm with LLMs tends to converge on a limited set of ideas, potentially stifling creative exploration.”
From Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking (2025) · cites this paper as [59]
The Introduction describes limitations of linear conversational LLM interfaces for nonlinear information exploration, and the Discussion reports that Sensecape users explored more broadly than users of the baseline interface. The paper does not establish that current interactions tend to converge on a limited set of ideas or specifically stifle creative exploration. (LLM, conf=0.82)
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- Authors
- Suh, S., Min, B., Palani, S., & Xia, H.
- Affiliations
- University of California San Diego (US)
- Venue
- Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2023-10-29
- Pages
- 1-18
- OpenAlex
- W4377371585
- Cited by
- 151
- References
- 57
- Citations by year
- 2026: 45 · 2025: 79 · 2024: 27
- FWCI
- 16.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- large language models, sensemaking, information exploration, knowledge organization, foraging, nonlinear interactions, information complexity, knowledge hierarchies
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