mlsanityData to 7 Oct 2026

Do LLMs Know to Respect Copyright Notice?

arXiv 2411.01136 ↗First posted 2 Nov 2024cs.CLcited by 12

Not on the map. Why?

Abstract (arXiv)

Prior study shows that LLMs sometimes generate content that violates copyright. In this paper, we study another important yet underexplored problem, i.e., will LLMs respect copyright information in user input, and behave accordingly? The research problem is critical, as a negative answer would imply that LLMs will become the primary facilitator and accelerator of copyright infringement behavior. We conducted a series of experiments using a diverse set of language models, user prompts, and copyrighted materials, including books, news articles, API documentation, and movie scripts. Our study offers a conservative evaluation of the extent to which language models may infringe upon copyrights when processing user input containing protected material. This research emphasizes the need for further investigation and the importance of ensuring LLMs respect copyright regulations when handling user input to prevent unauthorized use or reproduction of protected content. We also release a benchmark dataset serving as a test bed for evaluating infringement behaviors by LLMs and stress the need for future alignment.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 2 Nov 2024.

Builds on

23 of 36 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  11. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  12. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
11 more
  1. 2019How multilingual is Multilingual BERT?cited by 642
  2. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  3. 2021Protecting Intellectual Property of Language Generation APIs with Lexical Watermarkcited by 78
  4. 2023Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermarkcited by 61
  5. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
  6. 2023Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuningcited by 40
  7. 2023Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Modelscited by 35
  8. 2020Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluationscited by 30
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  10. 2024A First Look at GPT Apps: Landscape and Vulnerabilitycited by 15
  11. 2024Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Modelscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 10; 2026: 2.

12 papers.