@inproceedings{karamolegkou-etal-2023-copyright,
title = "Copyright Violations and Large Language Models",
author = "Karamolegkou, Antonia and
Li, Jiaang and
Zhou, Li and
S{\o}gaard, Anders",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.458",
doi = "10.18653/v1/2023.emnlp-main.458",
pages = "7403--7412",
abstract = "Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyrighted materials, rather than \textit{verbatim} reproduction. This work explores the issue of copyright violations and large language models through the lens of verbatim memorization, focusing on possible redistribution of copyrighted text. We present experiments with a range of language models over a collection of popular books and coding problems, providing a conservative characterization of the extent to which language models can redistribute these materials. Overall, this research highlights the need for further examination and the potential impact on future developments in natural language processing to ensure adherence to copyright regulations. Code is at https://github.com/coastalcph/CopyrightLLMs.",
}
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<abstract>Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyrighted materials, rather than verbatim reproduction. This work explores the issue of copyright violations and large language models through the lens of verbatim memorization, focusing on possible redistribution of copyrighted text. We present experiments with a range of language models over a collection of popular books and coding problems, providing a conservative characterization of the extent to which language models can redistribute these materials. Overall, this research highlights the need for further examination and the potential impact on future developments in natural language processing to ensure adherence to copyright regulations. Code is at https://github.com/coastalcph/CopyrightLLMs.</abstract>
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%0 Conference Proceedings
%T Copyright Violations and Large Language Models
%A Karamolegkou, Antonia
%A Li, Jiaang
%A Zhou, Li
%A Søgaard, Anders
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F karamolegkou-etal-2023-copyright
%X Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyrighted materials, rather than verbatim reproduction. This work explores the issue of copyright violations and large language models through the lens of verbatim memorization, focusing on possible redistribution of copyrighted text. We present experiments with a range of language models over a collection of popular books and coding problems, providing a conservative characterization of the extent to which language models can redistribute these materials. Overall, this research highlights the need for further examination and the potential impact on future developments in natural language processing to ensure adherence to copyright regulations. Code is at https://github.com/coastalcph/CopyrightLLMs.
%R 10.18653/v1/2023.emnlp-main.458
%U https://aclanthology.org/2023.emnlp-main.458
%U https://doi.org/10.18653/v1/2023.emnlp-main.458
%P 7403-7412
Markdown (Informal)
[Copyright Violations and Large Language Models](https://aclanthology.org/2023.emnlp-main.458) (Karamolegkou et al., EMNLP 2023)
ACL
- Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard. 2023. Copyright Violations and Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7403–7412, Singapore. Association for Computational Linguistics.