@inproceedings{rosa-etal-2025-impact,
title = "The Impact of Copyrighted Material on Large Language Models: {A} {Norwegian} Perspective",
author = "de la Rosa, Javier and
Mikhailov, Vladislav and
Zhang, Lemei and
Wetjen, Freddy and
Samuel, David and
Liu, Peng and
Braaten, Rolv-Arild and
M{\ae}hlum, Petter and
Birkenes, Magnus Breder and
Kutuzov, Andrey and
Enstad, Tita and
Farseth{\r{a}}s, Hans Christian and
Brygfjeld, Svein Arne and
Gulla, Jon Atle and
Oepen, Stephan and
Velldal, Erik and
{\O}stgulen, Wilfred and
{\O}vrelid, Lilja and
Myhre, Aslak Sira",
editor = "Johansson, Richard and
Stymne, Sara",
booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2025.nodalida-1.59/",
pages = "544--560",
ISBN = "978-9908-53-109-0",
abstract = "The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we found that adding both books and newspapers to the data mixture of LLMs tend to improve their performance, while the addition of fiction works seems to be detrimental. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development."
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<abstract>The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we found that adding both books and newspapers to the data mixture of LLMs tend to improve their performance, while the addition of fiction works seems to be detrimental. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.</abstract>
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%0 Conference Proceedings
%T The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
%A de la Rosa, Javier
%A Mikhailov, Vladislav
%A Zhang, Lemei
%A Wetjen, Freddy
%A Samuel, David
%A Liu, Peng
%A Braaten, Rolv-Arild
%A Mæhlum, Petter
%A Birkenes, Magnus Breder
%A Kutuzov, Andrey
%A Enstad, Tita
%A Farsethås, Hans Christian
%A Brygfjeld, Svein Arne
%A Gulla, Jon Atle
%A Oepen, Stephan
%A Velldal, Erik
%A Østgulen, Wilfred
%A Øvrelid, Lilja
%A Myhre, Aslak Sira
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F rosa-etal-2025-impact
%X The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we found that adding both books and newspapers to the data mixture of LLMs tend to improve their performance, while the addition of fiction works seems to be detrimental. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.
%U https://aclanthology.org/2025.nodalida-1.59/
%P 544-560
Markdown (Informal)
[The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective](https://aclanthology.org/2025.nodalida-1.59/) (de la Rosa et al., NoDaLiDa 2025)
ACL
- Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang, Freddy Wetjen, David Samuel, Peng Liu, Rolv-Arild Braaten, Petter Mæhlum, Magnus Breder Birkenes, Andrey Kutuzov, Tita Enstad, Hans Christian Farsethås, Svein Arne Brygfjeld, Jon Atle Gulla, Stephan Oepen, Erik Velldal, Wilfred Østgulen, Lilja Øvrelid, and Aslak Sira Myhre. 2025. The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 544–560, Tallinn, Estonia. University of Tartu Library.