The Impact of Copyrighted Material on Large Language Models: A Norwegian
  Perspective
Authors: name: Javier de la Rosa, name: Vladislav Mikhailov, name: Lemei Zhang, name: Freddy Wetjen, name: David Samuel, name: Peng Liu, name: Rolv-Arild Braaten, name: Petter Mæhlum, name: Magnus Breder Birkenes, name: Andrey Kutuzov, name: Tita Enstad, name: Svein Arne Brygfjeld, name: Jon Atle Gulla, name: Stephan Oepen, name: Erik Velldal, name: Wilfred Østgulen, name: Liljia Øvrelid, name: Aslak Sira Myhre

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.