# Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models

arXiv: https://arxiv.org/abs/2305.11483 · HTML: https://mlsanity.com/paper/2305.11483/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 75 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 3 of 3)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping for Code-Driven Storytelling](https://mlsanity.com/paper/2208.12981/) (2022)
- [Fuse: In-Situ Sensemaking Support in the Browser](https://mlsanity.com/paper/2208.14861/) (2022)

## Built on by (most cited first, 30 of 48)

- [OpenAgents: An Open Platform for Language Agents in the Wild](https://mlsanity.com/paper/2310.10634/) (2023)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization](https://mlsanity.com/paper/2408.01703/) (2024)
- [Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition](https://mlsanity.com/paper/2407.02651/) (2024)
- [RELIC: Investigating Large Language Model Responses using Self-Consistency](https://mlsanity.com/paper/2311.16842/) (2023)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions](https://mlsanity.com/paper/2310.01331/) (2023)
- [VeriPlan: Integrating Formal Verification and LLMs into End-User Planning](https://mlsanity.com/paper/2502.17898/) (2025)
- [Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses](https://mlsanity.com/paper/2312.00763/) (2023)
- [Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow](https://mlsanity.com/paper/2402.09894/) (2024)
- [One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations](https://mlsanity.com/paper/2405.05581/) (2024)
- [Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making](https://mlsanity.com/paper/2503.18419/) (2025)
- [SituationAdapt: Contextual UI Optimization in Mixed Reality with Situation Awareness via LLM Reasoning](https://mlsanity.com/paper/2409.12836/) (2024)
- [AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools](https://mlsanity.com/paper/2502.18736/) (2025)
- [JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models](https://mlsanity.com/paper/2404.08793/) (2024)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)
- [Co-audit: tools to help humans double-check AI-generated content](https://mlsanity.com/paper/2310.01297/) (2023)
- [Generative AI Uses and Risks for Knowledge Workers in a Science Organization](https://mlsanity.com/paper/2501.16577/) (2025)
- [TableTalk: Scaffolding Spreadsheet Development with a Language Agent](https://mlsanity.com/paper/2502.09787/) (2025)
- [Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages](https://mlsanity.com/paper/2505.22831/) (2025)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks](https://mlsanity.com/paper/2410.24032/) (2024)

## Abstract (arXiv)

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.