# Do LLMs Know to Respect Copyright Notice?

arXiv: https://arxiv.org/abs/2411.01136 · HTML: https://mlsanity.com/paper/2411.01136/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Builds on (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Deduplicating Training Data Mitigates Privacy Risks in Language Models](https://mlsanity.com/paper/2202.06539/) (2022)
- [Protecting Intellectual Property of Language Generation APIs with Lexical Watermark](https://mlsanity.com/paper/2112.02701/) (2021)
- [Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark](https://mlsanity.com/paper/2305.10036/) (2023)
- [Training Data Extraction From Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2305.16157/) (2023)
- [Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning](https://mlsanity.com/paper/2305.11759/) (2023)
- [Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models](https://mlsanity.com/paper/2310.10449/) (2023)
- [Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations](https://mlsanity.com/paper/2011.13564/) (2020)
- [Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition](https://mlsanity.com/paper/2401.12599/) (2024)
- [A First Look at GPT Apps: Landscape and Vulnerability](https://mlsanity.com/paper/2402.15105/) (2024)
- [Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models](https://mlsanity.com/paper/2404.08030/) (2024)

## Built on by (most cited first, 13 of 13)

- [Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?](https://mlsanity.com/paper/2512.21871/) (2025)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs](https://mlsanity.com/paper/2506.11059/) (2025)
- [Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts](https://mlsanity.com/paper/2509.04500/) (2025)
- [Beyond English: Unveiling Multilingual Bias in LLM Copyright Compliance](https://mlsanity.com/paper/2503.05713/) (2025)
- [ISACL: Internal State Analyzer for Copyrighted Training Data Leakage](https://mlsanity.com/paper/2508.17767/) (2025)
- [LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories](https://mlsanity.com/paper/2509.20909/) (2025)
- [NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation](https://mlsanity.com/paper/2504.18147/) (2025)
- [N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator](https://mlsanity.com/paper/2511.14195/) (2025)
- [Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks](https://mlsanity.com/paper/2602.05252/) (2026)
- [Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development](https://mlsanity.com/paper/2511.20623/) (2025)
- [SWAN: Semantic Watermarking with Abstract Meaning Representation](https://mlsanity.com/paper/2605.04305/) (2026)
- [What the Model Cannot See: Covert and Provable Text Invisibility in LLMs](https://mlsanity.com/paper/2610.15883/) (2026)

## 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.