The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
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arXiv
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| Format: | Preprint |
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2024
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| author | 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 Farsethås, Hans Christian Brygfjeld, Svein Arne Gulla, Jon Atle Oepen, Stephan Velldal, Erik Østgulen, Wilfred Øvrelid, Liljia Myhre, Aslak Sira |
| author_facet | 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 Farsethås, Hans Christian Brygfjeld, Svein Arne Gulla, Jon Atle Oepen, Stephan Velldal, Erik Østgulen, Wilfred Øvrelid, Liljia Myhre, Aslak Sira |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09460 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Farsethås, Hans Christian Brygfjeld, Svein Arne Gulla, Jon Atle Oepen, Stephan Velldal, Erik Østgulen, Wilfred Øvrelid, Liljia Myhre, Aslak Sira Computation and Language 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. |
| title | The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.09460 |