# Graphologue: Exploring Large Language Model Responses with Interactive Diagrams

arXiv: https://arxiv.org/abs/2305.11473 · HTML: https://mlsanity.com/paper/2305.11473/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 107 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 25 of 25)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text](https://mlsanity.com/paper/2107.03444/) (2021)
- [Text to 3D Scene Generation with Rich Lexical Grounding](https://mlsanity.com/paper/1505.06289/) (2015)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [Iris: A Conversational Agent for Complex Tasks](https://mlsanity.com/paper/1707.05015/) (2017)
- [Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts](https://mlsanity.com/paper/2208.01780/) (2022)
- [Automatically Generating CS Learning Materials with Large Language Models](https://mlsanity.com/paper/2212.05113/) (2022)
- [Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation](https://mlsanity.com/paper/1712.02007/) (2017)
- [Wigglite: Low-cost Information Collection and Triage](https://mlsanity.com/paper/2208.00496/) (2022)
- [Why More Text is (Often) Better: Themes from Reader Preferences for Integration of Charts and Text](https://mlsanity.com/paper/2209.10789/) (2022)
- [CrossCode: Multi-level Visualization of Program Execution](https://mlsanity.com/paper/2304.03445/) (2023)

## Built on by (most cited first, 30 of 66)

- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://mlsanity.com/paper/2407.04121/) (2024)
- [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)
- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [Supporting Sensemaking of Large Language Model Outputs at Scale](https://mlsanity.com/paper/2401.13726/) (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)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses](https://mlsanity.com/paper/2312.00763/) (2023)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow](https://mlsanity.com/paper/2402.09894/) (2024)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (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)
- [Conformal Information Pursuit for Interactively Guiding Large Language Models](https://mlsanity.com/paper/2507.03279/) (2025)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces](https://mlsanity.com/paper/2510.22922/) (2025)
- [Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming](https://mlsanity.com/paper/2407.20712/) (2024)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design](https://mlsanity.com/paper/2502.09867/) (2025)
- [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)
- [Generative Interfaces for Language Models](https://mlsanity.com/paper/2508.19227/) (2025)
- [Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages](https://mlsanity.com/paper/2505.22831/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented ability to synthesize text responses to diverse user questions. However, LLMs like ChatGPT present significant limitations in supporting complex information tasks due to the insufficient affordances of the text-based medium and linear conversational structure. Through a formative study with ten participants, we found that LLM interfaces often present long-winded responses, making it difficult for people to quickly comprehend and interact flexibly with various pieces of information, particularly during more complex tasks. We present Graphologue, an interactive system that converts text-based responses from LLMs into graphical diagrams to facilitate information-seeking and question-answering tasks. Graphologue employs novel prompting strategies and interface designs to extract entities and relationships from LLM responses and constructs node-link diagrams in real-time. Further, users can interact with the diagrams to flexibly adjust the graphical presentation and to submit context-specific prompts to obtain more information. Utilizing diagrams, Graphologue enables graphical, non-linear dialogues between humans and LLMs, facilitating information exploration, organization, and comprehension.