The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective

Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang, freddy wetjen, David Samuel, Peng Liu, Rolv-Arild, Petter Mæhlum, Magnus Breder Birkenes, Andrey Kutuzov, Tita Enstad, Svein Arne Brygfjeld, Jon Atle Gulla, Stephan Oepen, Erik Velldal, Wilfred Østgulen, Liljia Øvrelid, Aslak Sira Myhre

The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective: 7 upvotes on Hugging Face Daily Papers, #23 of 26 papers on 2024-12-13. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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