mlsanityData to 7 Oct 2026

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

arXiv 2412.09460 ↗First posted 12 Dec 2024cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 12 Dec 2024.

Builds on

27 of 42 references.

References · 27

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15 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmarkcited by 7
  2. 2025Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variationcited by 6
  3. 2025A Collection of Question Answering Datasets for Norwegiancited by 2
  4. 2025If I Could Turn Back Time: Temporal Reframing as a Historical Reasoning Task for LLMscited by 1
  5. 2026Measuring Task-Agnostic Training Data Influence Across Language Model Pretrainingcited by 1
  6. 2025Fluent Alignment with Disfluent Judges: Post-training for Lower-resource Languagescited by 0
  7. 2026NorwAI's Large Language Models: Technical Reportcited by 0
  8. 2026Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Trainingcited by 0