mlsanityData to 7 Oct 2026

What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus

arXiv 2105.02732 ↗First posted 6 May 2021cs.CLcited by 79

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Abstract (arXiv)

Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models. We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures. We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). Builds on 43 of its 72 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

43 of 72 references.

References · 43

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31 more
  1. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  2. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  3. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 12; 2023: 20; 2024: 25; 2025: 14; 2026: 4.

79 papers.

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  2. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 276
  3. 2023LLM Self Defense: By Self Examination, LLMs Know They Are Being Trickedcited by 226
  4. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  5. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  6. 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 147
  7. 2022Towards a Cleaner Document-Oriented Multilingual Crawled Corpuscited by 143
  8. 2022Impact of Pretraining Term Frequencies on Few-Shot Reasoningcited by 142
  9. 2023Auditing large language models: a three-layered approachcited by 135
  10. 2023On the Risk of Misinformation Pollution with Large Language Modelscited by 124
  11. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 107
  12. 2024A Survey of Multimodal Large Language Model from A Data-centric Perspectivecited by 73
38 more
  1. 2023MathPile: A Billion-Token-Scale Pretraining Corpus for Mathcited by 67
  2. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  3. 2023LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Modelscited by 46
  4. 2024Consent in Crisis: The Rapid Decline of the AI Data Commonscited by 45
  5. 2024IrokoBench: A New Benchmark for African Languages in the Age of Large Language Modelscited by 43
  6. 2024RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMscited by 42
  7. 2022BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Banglacited by 40
  8. 2021CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairscited by 39
  9. 2023SoK: Memorization in General-Purpose Large Language Modelscited by 36
  10. 2023Lost in Translation: Large Language Models in Non-English Content Analysiscited by 34
  11. 2022Data Governance in the Age of Large-Scale Data-Driven Language Technologycited by 32
  12. 2023The ROOTS Search Tool: Data Transparency for LLMscited by 30
  13. 2023Data Portraits: Recording Foundation Model Training Datacited by 29
  14. 2023CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Datacited by 21
  15. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  16. 2025Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Trainingcited by 19
  17. 2021A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communicationcited by 18
  18. 2024GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Modelscited by 18
  19. 2023ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Modelcited by 16
  20. 2024Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotationcited by 16
  21. 2024On Catastrophic Inheritance of Large Foundation Modelscited by 15
  22. 2024Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompcited by 15
  23. 2024Toxicity Detection for Freecited by 14
  24. 2023Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defensecited by 13
  25. 2023GIO: Gradient Information Optimization for Training Dataset Selectioncited by 13
  26. 2023Scaling Laws Do Not Scalecited by 13
  27. 2022Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resourcescited by 11
  28. 2022Language Detoxification with Attribute-Discriminative Latent Spacecited by 11
  29. 2022PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Modelcited by 9
  30. 2023Continual Learning Under Language Shiftcited by 9
  31. 2023GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Explorationcited by 8
  32. 2024LOCOST: State-Space Models for Long Document Abstractive Summarizationcited by 8
  33. 2024Beyond the Safety Bundle: Auditing the Helpful and Harmless Datasetcited by 8
  34. 2025Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMscited by 8
  35. 2023The Web Can Be Your Oyster for Improving Large Language Modelscited by 7
  36. 2024Time Machine GPTcited by 7
  37. 2024A Review of the Challenges with Massive Web-mined Corpora Used in Large Language Models Pre-Trainingcited by 7
  38. 2024Toxicity of the Commons: Curating Open-Source Pre-Training Datacited by 7

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