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Deduplicating Training Data Makes Language Models Better
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Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
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“Data deduplication has become a key tool for language model projects following research showing that it both improves performance on downstream tasks (Lee et al., 2022; Zhang et al., 2021) and decreases memorization of training data (Kandpal et al., 2022).”
From The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) · cites this paper as Lee et al., 2022
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.
In the citing paper
“To ensure reliable evaluation and improve performance [26], we deduplicate documents by title and utilize the predefined training and validation splits.”
From Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval (2025) · cites this paper as [26]
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“For example, the probability of reproducing training data has been shown to correlate with its frequency of appearance [12].”
From Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy (2025) · cites this paper as [18]
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“Deduplication is a powerful strategy for improving dataset quality (Li et al., 2024; Lee et al., 2022; Penedo et al., 2024b; Fang et al., 2025; Liu et al., 2024).”
From OpenThoughts: Data Recipes for Reasoning Models (2025) · cites this paper as Lee et al., 2022
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“Moreover, it is provided only on the complete text generated by the model (trajectory) and not on specific parts of the text (particular state) [92,138].”
From RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs (2026) · cites this paper as [92]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Space/time trade-offs in hash coding with allowable errors
Bloom, B. (1970) · CACM · doi:10.1145/362686.362692 · Cited by 8,424
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 4×
“And so because these datasets are up to 19% smaller, even including the deduplication runtime itself, training on dedupli-cated datasets directly reduces the training cost in terms of time, dollar, and the environment (Bender et al., 2021; Strubell et al., 2019; Patterson et al., 2021).”
BackgroundInfluentialMembership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE. 1
Jaccard, P. (1912) · New Phytologist · doi:10.1111/j.1469-8137.1912.tb05611.x · Cited by 4,561
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469
Flagged first, then by citations
Research works printed but not linked: 7
- PMLR.
- David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003. English gigaword. Linguistic Data Consortium, Philadelphia, 4(1):34.
- Paul Jaccard. 1912. The distribution of the flora in the alpine zone. New phytologist, 11(2):37-50. 10 8433
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Colin Raffel, Noam Shazeer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to- text transformer. Journal of Machine Learning Re- search, 21(140):1-67.
- PMLR.
- Yannick Versley and Yana Panchenko. 2012. Not just bigger: Towards better-quality web corpora. In Pro- ceedings of the seventh Web as Corpus Workshop (WAC7), pages 44-52.
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Mozo, A., Gálvez, S., Christou, I. T., Vogiatzis, D., Navarro, T., & Valverde, F. L. (2025) · IEEE Access · doi:10.1109/access.2025.3605022 · Cited by 4
Large Vision-Language Model Security: A Survey
Wang, T., Fang, Z., Xue, H., Zhang, C., Jin, M., Xu, W., Shu, D., Yang, S. J., Wang, Z. G., & Liu, D. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0151-6_1 · Cited by 4
Self-citationUbiquity of LLM Hallucinations Across Critical Domains: A Survey
Singh, D., & Singh, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8197-6_9 · Cited by 4
Incorporating RAG for Factual Hallucination Detection Modeling in Intelligent Educational system
Zheng, L., Ye, J., Zhao, G. L., Luo, S., Nan, M., & Xie, Y. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092000 · Cited by 4
AI in Biodiversity Education: The Bias in Endangered Species Information and Its Implications
Noriega, L. D. P., Bobo‐Pinilla, J., Iglesias, J. D., Reinoso, R., Gallego, A. M., & y Alpera, S. Q. (2025) · Sustainability · doi:10.3390/su17146554 · Cited by 5
Evaluating real-world deployment of an HL7-CDA-aligned LLM for ICD-10-CM coding
Dai, H.-J., Li, Z., Lu, A.-T., Su, M., Shain, B.-T., Li, M.-T., Wang, L. Y., Wang, K., Liu, P., Doan, V. T., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02541-5 · Cited by 5
Transferability of a Sensing Mattress for Posture Classification from Research into Clinics
Gnarra, O., Breuss, A., Rossi, L. A., Fujs, M., Knobel, S. E. J., Warncke, J. D., Gerber, S. M., Bassetti, C. L., Riener, R., Nef, T., et al. (2023) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr58425.2023.10304684 · Cited by 4
Range Membership Inference Attacks
Tao, J., & Shokri, R. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00026 · Cited by 4
Osobase, K. O., Akadiri, O. O., Dirisu, C. D., Asolo, E., Adewara, D. E., & Ekechi, C. C. (2025) · Asian Journal of Basic Science & Research · doi:10.38177/ajbsr.2025.7312 · Cited by 4
Almeida, T. S., Nogueira, R. F., & Pedrini, H. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5788 · Cited by 8
esCorpius: A Massive Spanish Crawling Corpus
Gutiérrez-Fandiño, A., Pérez‐Fernández, D., Armengol-Estapé, J., Griol, D., & Callejas, Z. (2022) · IberSPEECH · doi:10.21437/iberspeech.2022-26 · Cited by 5
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3
“To ensure reliable evaluation and improve performance [26], we deduplicate documents by title and utilize the predefined training and validation splits.”
Evaluation of Document Deduplication Algorithms for Large Text Corpora
Leveling, J., Helmer, L., Stein, B. J., Wegener, D., Sheikh, Z., Fernandes, E., & Abdelwahab, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82481-4_27 · Cited by 4
Semi-Automatic Hierarchical Taxonomy Creation from Existing Taxonomies with Large Language Models
Motamedi, E., Novalija, I., & Rei, L. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00982-y · Cited by 3
Trustworthiness of large language models: hallucinations
Brunello, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33592-1.00007-3 · Cited by 4
Foundation Models for Autonomous Driving Systems: An Initial Roadmap
Wu, X., Cheng, M., Ren, X., Hu, Q., Chen, J., Huang, Y., Cordy, M., Zhang, Y., Xie, X., Ma, L., et al. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3807455 · Cited by 6
Zhang, J., Wang, F. (., Zhang, C., Liu, Y., Lu, J., & Zhao, Y. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104794 · Cited by 4
Self-citationAssessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Tang, R., Lueck, G., Quispe, R., Inan, H. A., Kulkarni, J., & Hu, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1029 · Cited by 3
InDi: Informative and Diverse Sampling for Dense Retrieval
Cohen, N., Indelman, H. C., Fairstein, Y., & Kushilevitz, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56063-7_16 · Cited by 3
Fei, 刘. L., Yanfen, 钟. Z., & Jiawei, 邱. Q. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop240672 · Cited by 3
Iana, A., Schmidt, F. D., Glavaš, G., & Paulheim, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88711-6_8 · Cited by 7
Kundačina, O., Vincan, V., & Mišković, D. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3565216 · Cited by 4
Training Text-to-Text Transformers with Privacy Guarantees
Ponomareva, N., Bastings, J., & Vassilvitskii, S. (2022) · Workshop on Privacy in Natural Language Processing (PrivateNLP) · doi:10.18653/v1/2022.privatenlp-1.4 · Cited by 3
MFinBERT: Multilingual Pretrained Language Model For Financial Domain
Nguyen, D., Cao, N., Nguyen, S., Ta, S., & Dinh, C. (2022) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse56063.2022.9953749 · Cited by 3
North Korean Neural Machine Translation through South Korean Resources
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Exploring the Topics of Audio Words for Detecting Alzheimer's Disease From Spontaneous Speech
Guo, Z., & Ling, Z.-H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3334696 · Cited by 3
Metadata
- Authors
- Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N.
- Affiliations
- Google (United States) (US)
- University of Pennsylvania (US)
- Corresponding author
- Ippolito, Daphne
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 8424-8445
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3177765786
- Cited by
- 304
- References
- 50
- Citations by year
- 2026: 62 · 2025: 79 · 2024: 65 · 2023: 64 · 2022: 27
- FWCI
- 32.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- language model training, training data quality, duplicate removal
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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