# WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild

arXiv: https://arxiv.org/abs/2409.03753 · HTML: https://mlsanity.com/paper/2409.03753/

## 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 (most cited first, 10 of 10)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [What's In My Big Data?](https://mlsanity.com/paper/2310.20707/) (2023)
- [Parametric UMAP embeddings for representation and semi-supervised learning](https://mlsanity.com/paper/2009.12981/) (2020)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community](https://mlsanity.com/paper/2408.08291/) (2024)
- [StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions](https://mlsanity.com/paper/2407.12423/) (2024)
- [MiniConf -- A Virtual Conference Framework](https://mlsanity.com/paper/2007.12238/) (2020)

## Built on by (most cited first, 20 of 20)

- [Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer](https://mlsanity.com/paper/2502.12964/) (2025)
- [VisionArena: 230K Real World User-VLM Conversations with Preference Labels](https://mlsanity.com/paper/2412.08687/) (2024)
- [QSpec: Speculative Decoding with Complementary Quantization Schemes](https://mlsanity.com/paper/2410.11305/) (2024)
- [Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions](https://mlsanity.com/paper/2502.04322/) (2025)
- [Intelligence per Watt: Measuring Intelligence Efficiency of Local AI](https://mlsanity.com/paper/2511.07885/) (2025)
- [SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia](https://mlsanity.com/paper/2502.06298/) (2025)
- [Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants](https://mlsanity.com/paper/2605.09808/) (2026)
- [A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs](https://mlsanity.com/paper/2505.23816/) (2025)
- [Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts](https://mlsanity.com/paper/2507.04569/) (2025)
- [UnsafeChain: Enhancing Reasoning Model Safety via Hard Cases](https://mlsanity.com/paper/2507.21652/) (2025)
- [HACK: Hallucinations Along Certainty and Knowledge Axes](https://mlsanity.com/paper/2510.24222/) (2025)
- [From Chat Logs to Collective Insights: Aggregative Question Answering](https://mlsanity.com/paper/2505.23765/) (2025)
- [PAM: Training Policy-Aligned Moderation Filters at Scale](https://mlsanity.com/paper/2505.19766/) (2025)
- [A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents](https://mlsanity.com/paper/2601.10809/) (2026)
- [Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild](https://mlsanity.com/paper/2605.29018/) (2026)
- [Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue](https://mlsanity.com/paper/2508.07520/) (2025)
- [How Does Prefix Matter in Reasoning Model Tuning?](https://mlsanity.com/paper/2601.01624/) (2026)
- [Tail-Aware Information-Theoretic Bounds for LLM Alignment under Heavy-Tailed Rewards](https://mlsanity.com/paper/2604.10727/) (2026)
- [CausalMix: Data Mixture as Causal Inference for Language Model Training](https://mlsanity.com/paper/2607.01104/) (2026)
- [LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions](https://mlsanity.com/paper/2609.14849/) (2026)

## 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.