WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild
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Abstract (arXiv)
The increasing availability of real-world conversation data offers exciting opportunities for researchers to study user-chatbot interactions. However, the sheer volume of this data makes manually examining individual conversations impractical. To overcome this challenge, we introduce WildVis, an interactive tool that enables fast, versatile, and large-scale conversation analysis. WildVis provides search and visualization capabilities in the text and embedding spaces based on a list of criteria. To manage million-scale datasets, we implemented optimizations including search index construction, embedding precomputation and compression, and caching to ensure responsive user interactions within seconds. We demonstrate WildVis' utility through three case studies: facilitating chatbot misuse research, visualizing and comparing topic distributions across datasets, and characterizing user-specific conversation patterns. WildVis is open-source and designed to be extendable, supporting additional datasets and customized search and visualization functionalities.
Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 5 Sep 2024.
Builds on
10 of 14 references.
References · 10
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
- 2021Datasets: A Community Library for Natural Language Processingcited by 530
- 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 461
- 2023What's In My Big Data?cited by 129
- 2020Parametric UMAP embeddings for representation and semi-supervised learningcited by 104
- 2020ConvoKit: A Toolkit for the Analysis of Conversationscited by 72
- 2024The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Communitycited by 8
- 2024StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactionscited by 7
- 2020MiniConf -- A Virtual Conference Frameworkcited by 2
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Citing papers per year: 2024: 2; 2025: 11; 2026: 7.
20 papers.
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- 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 27
- 2024VisionArena: 230K Real World User-VLM Conversations with Preference Labelscited by 22
- 2024QSpec: Speculative Decoding with Complementary Quantization Schemescited by 18
- 2025Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactionscited by 15
- 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AIcited by 14
- 2025SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asiacited by 13
- 2026Quantifying the Utility of User Simulators for Building Collaborative LLM Assistantscited by 10
- 2025A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMscited by 7
- 2025Nile-Chat: Egyptian Language Models for Arabic and Latin Scriptscited by 7
- 2025UnsafeChain: Enhancing Reasoning Model Safety via Hard Casescited by 7
- 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
- 2025From Chat Logs to Collective Insights: Aggregative Question Answeringcited by 3
8 more
- 2025PAM: Training Policy-Aligned Moderation Filters at Scalecited by 2
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agentscited by 1
- 2026Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wildcited by 1
- 2025Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialoguecited by 0
- 2026How Does Prefix Matter in Reasoning Model Tuning?cited by 0
- 2026Tail-Aware Information-Theoretic Bounds for LLM Alignment under Heavy-Tailed Rewardscited by 0
- 2026CausalMix: Data Mixture as Causal Inference for Language Model Trainingcited by 0
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questionscited by 0
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions13 Sep 2026
- 2026CausalMix: Data Mixture as Causal Inference for Language Model Training1 Jul 2026
- 2026Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild27 May 2026
- 2026Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants10 May 2026
- 2026Tail-Aware Information-Theoretic Bounds for LLM Alignment under Heavy-Tailed Rewards12 Apr 2026
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents15 Jan 2026
- 2026How Does Prefix Matter in Reasoning Model Tuning?4 Jan 2026
- 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AI11 Nov 2025
- 2025HACK: Hallucinations Along Certainty and Knowledge Axes28 Oct 2025
- 2025Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue11 Aug 2025
- 2025UnsafeChain: Enhancing Reasoning Model Safety via Hard Cases29 Jul 2025
- 2025Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts6 Jul 2025
8 more
- 2025From Chat Logs to Collective Insights: Aggregative Question Answering29 May 2025
- 2025A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs27 May 2025
- 2025PAM: Training Policy-Aligned Moderation Filters at Scale26 May 2025
- 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer18 Feb 2025
- 2025SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia10 Feb 2025
- 2025Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions6 Feb 2025
- 2024VisionArena: 230K Real World User-VLM Conversations with Preference Labels11 Dec 2024
- 2024QSpec: Speculative Decoding with Complementary Quantization Schemes15 Oct 2024
- 2025PAM: Training Policy-Aligned Moderation Filters at Scalecited by 2
- 2025PAM: Training Policy-Aligned Moderation Filters at Scale26 May 2025
- 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 27
- 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AIcited by 14
- 2025SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asiacited by 13
- 2026Quantifying the Utility of User Simulators for Building Collaborative LLM Assistantscited by 10
- 2025Nile-Chat: Egyptian Language Models for Arabic and Latin Scriptscited by 7
- 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
- 2025From Chat Logs to Collective Insights: Aggregative Question Answeringcited by 3
- 2026Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wildcited by 1
- 2025Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialoguecited by 0
- 2026CausalMix: Data Mixture as Causal Inference for Language Model Trainingcited by 0
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questionscited by 0
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions13 Sep 2026
- 2026CausalMix: Data Mixture as Causal Inference for Language Model Training1 Jul 2026
- 2026Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild27 May 2026
- 2026Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants10 May 2026
- 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AI11 Nov 2025
- 2025HACK: Hallucinations Along Certainty and Knowledge Axes28 Oct 2025
- 2025Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue11 Aug 2025
- 2025Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts6 Jul 2025
- 2025From Chat Logs to Collective Insights: Aggregative Question Answering29 May 2025
- 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer18 Feb 2025
- 2025SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia10 Feb 2025