The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
de la Rosa, Javier, Mikhailov, Vladislav, Zhang, Lemei, Wetjen, Freddy, Samuel, David, Liu, Peng, Braaten, Rolv-Arild, Mæhlum, Petter, Birkenes, Magnus Breder, Kutuzov, Andrey, Enstad, Tita, Brygfjeld, Svein Arne, Gulla, Jon Atle, Oepen, Stephan, Velldal, Erik, Østgulen, Wilfred, Øvrelid, Liljia, Myhre, Aslak Sira
–arXiv.org Artificial Intelligence
The use of copyrighted materials in training generative language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of copyrighted materials on the performance of large language models (LLMs) for Norwegian. We found that both books and newspapers contribute positively when the models are evaluated on a diverse set of Norwegian benchmarks, while fiction works possibly lead to decreased performance. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.
arXiv.org Artificial Intelligence
Dec-12-2024
- Country:
- Europe (1.00)
- North America > United States
- Minnesota (0.28)
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- Research Report (1.00)
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