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The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
Computation and Language · Submitted 12 Dec 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The use of copyrighted materials in training language models raises critical legal and ethical questions. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | 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. | contribution 1.00 | 0.85 | 0.06 | asserted 0.97 |
| s2 | 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. | result 1.00 | 0.96 | 0.80 | asserted 0.98 |
| s3 | Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development. | interpretation 0.86 | 0.20 | 0.08 | hedged possibility 1.00 |
What was examined, and what was not
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The paper
Original 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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How the literature uses it
1 arXiv paper cite this in 1 sentence
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Uses the method or tool of 1 paper
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The training phase involved multiple models, each based on the Mistral architecture [ref] .
Mistral 7B · Model Training · 0.89
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