mlsanityData to 7 Oct 2026

WildChat: 1M ChatGPT Interaction Logs in the Wild

arXiv 2405.01470 ↗First posted 2 May 2024cs.CLcited by 598

Not on the map. Why?

Cited by 89 in LLM alignment / RLHF / preference optimisation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Chatbots such as GPT-4 and ChatGPT are now serving millions of users. Despite their widespread use, there remains a lack of public datasets showcasing how these tools are used by a population of users in practice. To bridge this gap, we offered free access to ChatGPT for online users in exchange for their affirmative, consensual opt-in to anonymously collect their chat transcripts and request headers. From this, we compiled WildChat, a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns. We compare WildChat with other popular user-chatbot interaction datasets, and find that our dataset offers the most diverse user prompts, contains the largest number of languages, and presents the richest variety of potentially toxic use-cases for researchers to study. In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses, alongside request headers. This augmentation allows for more detailed analysis of user behaviors across different geographical regions and temporal dimensions. Finally, because it captures a broad range of use cases, we demonstrate the dataset's potential utility in fine-tuning instruction-following models. WildChat is released at https://wildchat.allen.ai under AI2 ImpACT Licenses.

Key facts. Cited by 598 papers in the map (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

11 of 22 references.

References · 11

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  6. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  7. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  8. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 475
  9. 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 449
  10. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
  11. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 101; 2025: 214; 2026: 283.

598 papers.

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  1. 2024OpenAI o1 System Cardcited by 1,220
  2. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 988
  3. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  4. 2024From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipelinecited by 484
  5. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  6. 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 385
  7. 2024Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothingcited by 341
  8. 2024Improving Alignment and Robustness with Circuit Breakerscited by 302
  9. 2024Deliberative Alignment: Reasoning Enables Safer Language Modelscited by 292
  10. 2024WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wildcited by 211
  11. 2025Generalizing Verifiable Instruction Followingcited by 178
  12. 2024Distilling System 2 into System 1cited by 152
38 more
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  3. 2024Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoderscited by 133
  4. 2024OpenCoder: The Open Cookbook for Top-Tier Code Large Language Modelscited by 127
  5. 2025Checklists Are Better Than Reward Models For Aligning Language Modelscited by 115
  6. 2025OctoThinker: Mid-training Incentivizes Reinforcement Learning Scalingcited by 110
  7. 2024DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiencycited by 106
  8. 2024Rule Based Rewards for Language Model Safetycited by 106
  9. 2026AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolutioncited by 105
  10. 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
  11. 2024LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecturecited by 100
  12. 2025NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Modelcited by 94
  13. 2024Steering Language Model Refusal with Sparse Autoencoderscited by 89
  14. 2024VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Modelscited by 85
  15. 2025Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoningcited by 85
  16. 2025Jointly Reinforcing Diversity and Quality in Language Model Generationscited by 79
  17. 2025Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judgecited by 75
  18. 2024MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixturescited by 73
  19. 2025J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learningcited by 73
  20. 2025STAIR: Improving Safety Alignment with Introspective Reasoningcited by 72
  21. 2024Self-Taught Evaluatorscited by 70
  22. 2024Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questionscited by 70
  23. 2025STAR-1: Safer Alignment of Reasoning LLMs with 1K Datacited by 67
  24. 2024Imprompter: Tricking LLM Agents into Improper Tool Usecited by 66
  25. 2024Clio: Privacy-Preserving Insights into Real-World AI Usecited by 66
  26. 2025NoveltyBench: Evaluating Language Models for Humanlike Diversitycited by 64
  27. 2025Think Only When You Need with Large Hybrid-Reasoning Modelscited by 64
  28. 2024Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchycited by 62
  29. 2024Get my drift? Catching LLM Task Drift with Activation Deltascited by 61
  30. 2025Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitatecited by 61
  31. 2024RMB: Comprehensively Benchmarking Reward Models in LLM Alignmentcited by 60
  32. 2025PolyGuard: A Multilingual Safety Moderation Tool for 17 Languagescited by 57
  33. 2026Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Taskscited by 55
  34. 2024PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensemblescited by 54
  35. 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Abovecited by 51
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  37. 2025Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Modelscited by 49
  38. 2025ExGRPO: Learning to Reason from Experiencecited by 49

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