Katherine Lee,Daphne Ippolito,Andrew Nystrom,Chiyuan Zhang,Douglas Eck,Chris Callison-Burch,Nicholas Carlini
Abstract
We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets—for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times. Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer training steps to achieve the same or better accuracy. We can also reduce train-test overlap, which affects over 4% of the validation set of standard datasets, thus allowing for more accurate evaluation. Code for deduplication is released at https://github.com/google-research/deduplicate-text-datasets.
- Anthology ID:
- 2022.acl-long.577
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan,Preslav Nakov,Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8424–8445
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.577
- DOI:
- 10.18653/v1/2022.acl-long.577
- Bibkey:
- Cite (ACL):
- Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022. Deduplicating Training Data Makes Language Models Better. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8424–8445, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Deduplicating Training Data Makes Language Models Better (Lee et al., ACL 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.acl-long.577.pdf
- Video:
- https://aclanthology.org/2022.acl-long.577.mp4
- Code
- google-research/deduplicate-text-datasets+additional community code
- Data
- Billion Word Benchmark,RealNews,Wiki-40B
Export citation
- BibTeX
- MODS XML
- Endnote
- Preformatted
@inproceedings{lee-etal-2022-deduplicating, title = "Deduplicating Training Data Makes Language Models Better", author = "Lee, Katherine and Ippolito, Daphne and Nystrom, Andrew and Zhang, Chiyuan and Eck, Douglas and Callison-Burch, Chris and Carlini, Nicholas", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.577", doi = "10.18653/v1/2022.acl-long.577", pages = "8424--8445", abstract = "We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1{\%} of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets{---}for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times. Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer training steps to achieve the same or better accuracy. We can also reduce train-test overlap, which affects over 4{\%} of the validation set of standard datasets, thus allowing for more accurate evaluation. Code for deduplication is released at \url{https://github.com/google-research/deduplicate-text-datasets}.",}Download as File
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%0 Conference Proceedings%T Deduplicating Training Data Makes Language Models Better%A Lee, Katherine%A Ippolito, Daphne%A Nystrom, Andrew%A Zhang, Chiyuan%A Eck, Douglas%A Callison-Burch, Chris%A Carlini, Nicholas%Y Muresan, Smaranda%Y Nakov, Preslav%Y Villavicencio, Aline%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)%D 2022%8 May%I Association for Computational Linguistics%C Dublin, Ireland%F lee-etal-2022-deduplicating%X We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets—for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times. Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer training steps to achieve the same or better accuracy. We can also reduce train-test overlap, which affects over 4% of the validation set of standard datasets, thus allowing for more accurate evaluation. Code for deduplication is released at https://github.com/google-research/deduplicate-text-datasets.%R 10.18653/v1/2022.acl-long.577%U https://aclanthology.org/2022.acl-long.577%U https://doi.org/10.18653/v1/2022.acl-long.577%P 8424-8445
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Markdown (Informal)
[Deduplicating Training Data Makes Language Models Better](https://aclanthology.org/2022.acl-long.577) (Lee et al., ACL 2022)
- Deduplicating Training Data Makes Language Models Better (Lee et al., ACL 2022)
ACL
- Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022. Deduplicating Training Data Makes Language Models Better. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8424–8445, Dublin, Ireland. Association for Computational Linguistics.
