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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
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Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt Gardner. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
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“Typically trained on an enormous scale of uncurated Internet-based data, LLMs inherit stereotypes, misrepresentations, derogatory and exclusionary language, and other denigrating behaviors that disproportionately affect already-vulnerable and marginalized communities (Bender et al. 2021; Dodge et al. 2021; Sheng et al. 2021b).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Dodge et al. 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Typically trained on an enormous scale of uncurated Internet-based data, LLMs inherit stereotypes, misrepresentations, derogatory and exclusionary language, and other denigrating behaviors that disproportionately affect already-vulnerable and marginalized communities (Bender et al. 2021; Dodge et al. 2021; Sheng et al. 2021b).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Dodge et al. 2021
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“Very recent research - most published within the last year - has begun to analyze dataset values [18, 40], question assumptions around dataset use [100], unpack what is represented in ML datasets [43, 88, 124] and establish basics of how an organized dataset lifecycle might proceed [74].”
From Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) · cites this paper as [43]
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“LLM datasets are poorly documented with little understanding of their ultimate makeup62.”
From Medical large language models are vulnerable to data-poisoning attacks (2025) · cites this paper as [62]
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“The pre-release increase in specific terms could occur naturally and may already be reflected in the pretraining corpora of later LLMs, which were built from time-specific web snapshots (e.g., C4, April 2019 [31]).”
From Delving Into PubMed Records: How AI-Influenced Vocabulary has Transformed Medical Writing since ChatGPT (2025) · cites this paper as [31]
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“Access to pre-training data enables leakage detection through overlap analysis, as highlighted in previous research (Dodge et al., 2021;Deng et al., 2023); even without direct access, details like data sources and time frames can reveal leakages (Li, 2023a;b).”
From Benchmarking Benchmark Leakage in Large Language Models (2024) · cites this paper as Dodge et al., 2021
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Efficient Hierarchical Domain Adaptation for Pretrained Language Models
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Self-citationBitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration
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Bridging large language model disparities: skill tagging of multilingual educational content
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BBS: Bi-Directional Bit-Level Sparsity for Deep Learning Acceleration
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SwahBERT: Language Model of Swahili
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LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions
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Extensive evaluation of transformer-based architectures for adverse drug events extraction
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Self-citationFairPrism: Evaluating Fairness-Related Harms in Text Generation
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“This allows us to prevent simple discussion of sensitive topics from being conflated with the perpetuation of stereotyping or demeaning harms, an issue that erases marginalized voices and necessary discussion of real-world harms (Dodge et al., 2021; Schlesinger et al., 2018).”
Background
Metadata
- Authors
- Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 1286-1305
- OpenAlex
- W3204096139
- Cited by
- 306
- References
- 65
- Citations by year
- 2026: 76 · 2025: 77 · 2024: 54 · 2023: 65 · 2022: 33 · 2021: 1
- FWCI
- 17.67
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Allen Institute for Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- web crawling, corpus annotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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