Do LLMs Know to Respect Copyright Notice?
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
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11 more
- 2019How multilingual is Multilingual BERT?cited by 642
- 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
- 2021Protecting Intellectual Property of Language Generation APIs with Lexical Watermarkcited by 78
- 2023Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermarkcited by 61
- 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
- 2023Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuningcited by 40
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- 2024Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Modelscited by 9
Built on by
Citing papers per year: 2025: 10; 2026: 2.
12 papers.
- 2025Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?cited by 8
- 2025What Makes a Good Natural Language Prompt?cited by 6
- 2025CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMscited by 6
- 2025Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contextscited by 3
- 2025ISACL: Internal State Analyzer for Copyrighted Training Data Leakagecited by 2
- 2025LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectoriescited by 2
- 2025Beyond English: Unveiling Multilingual Bias in LLM Copyright Compliancecited by 1
- 2025NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptationcited by 1
- 2025N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluatorcited by 1
- 2026Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Riskscited by 1
- 2025Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Developmentcited by 0
- 2026SWAN: Semantic Watermarking with Abstract Meaning Representationcited by 0
- 2026SWAN: Semantic Watermarking with Abstract Meaning Representation5 May 2026
- 2026Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks5 Feb 2026
- 2025Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?26 Dec 2025
- 2025Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development25 Nov 2025
- 2025N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator18 Nov 2025
- 2025LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories25 Sep 2025
- 2025Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts2 Sep 2025
- 2025ISACL: Internal State Analyzer for Copyrighted Training Data Leakage25 Aug 2025
- 2025What Makes a Good Natural Language Prompt?7 Jun 2025
- 2025CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs27 May 2025
- 2025NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation25 Apr 2025
- 2025Beyond English: Unveiling Multilingual Bias in LLM Copyright Compliance14 Feb 2025