mlsanityData to 14 Oct 2026

Supporting Sensemaking of Large Language Model Outputs at Scale

arXiv 2401.13726 ↗First posted 24 Jan 2024cs.HC cs.LGcited by 27

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Abstract (arXiv)

Large language models (LLMs) are capable of generating multiple responses to a single prompt, yet little effort has been expended to help end-users or system designers make use of this capability. In this paper, we explore how to present many LLM responses at once. We design five features, which include both pre-existing and novel methods for computing similarities and differences across textual documents, as well as how to render their outputs. We report on a controlled user study (n=24) and eight case studies evaluating these features and how they support users in different tasks. We find that the features support a wide variety of sensemaking tasks and even make tasks previously considered to be too difficult by our participants now tractable. Finally, we present design guidelines to inform future explorations of new LLM interfaces.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 6 of its 67 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

6 of 67 references.

References · 6

  1. 2023The Falcon Series of Open Language Modelscited by 535
  2. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
  3. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  4. 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 66
  5. 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
  6. 2022LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexiacited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 15; 2026: 7.

27 papers.

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  1. 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 144
  2. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  3. 2025AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Toolscited by 8
  4. 2025OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Modelscited by 7
  5. 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
  6. 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptionscited by 6
  7. 2025CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writingcited by 5
  8. 2026From Words to Widgets for Controllable LLM Generationcited by 5
  9. 2024Large Language Models Meet User Interfaces: The Case of Provisioning Feedbackcited by 4
  10. 2025What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patternscited by 4
  11. 2024Bridging Dictionary: AI-Generated Dictionary of Partisan Language Usecited by 3
  12. 2025DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decompositioncited by 3
15 more
  1. 2025Evalet: Evaluating Large Language Models through Functional Fragmentationcited by 3
  2. 2025Improving User Interface Generation Models from Designer Feedbackcited by 3
  3. 2025Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answerscited by 3
  4. 2025ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Modelscited by 3
  5. 2025Understanding User Experiences of Computer Use Agents: Design Space and Opportunities for Building Agent UX Prototypescited by 2
  6. 2026Texterial: A Text-as-Material Interaction Paradigm for LLM-Mediated Writingcited by 2
  7. 2025HiLDe: Intentional Code Generation via Human-in-the-Loop Decodingcited by 1
  8. 2025Understanding Prompt Programming Tasks and Questionscited by 1
  9. 2026Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wildcited by 1
  10. 2025A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiologycited by 0
  11. 2025InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluationcited by 0
  12. 2026Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaborationcited by 0
  13. 2026Beyond One Output: Visualizing and Comparing Distributions of Language Model Generationscited by 0
  14. 2026Visual Fingerprints for LLM Generation Comparisoncited by 0
  15. 2026DimSteer: Steering LLM Authoring with Automatically Discovered Stylistic Controlscited by 0