# Beyond English: Unveiling Multilingual Bias in LLM Copyright Compliance

arXiv: https://arxiv.org/abs/2503.05713 · HTML: https://mlsanity.com/paper/2503.05713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Fine-grained Hallucination Detection and Editing for Language Models](https://mlsanity.com/paper/2401.06855/) (2024)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs](https://mlsanity.com/paper/2305.16339/) (2023)
- [Detecting and Mitigating Hallucinations in Multilingual Summarisation](https://mlsanity.com/paper/2305.13632/) (2023)
- [Evaluating Copyright Takedown Methods for Language Models](https://mlsanity.com/paper/2406.18664/) (2024)
- [CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation](https://mlsanity.com/paper/2407.07087/) (2024)
- [SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation](https://mlsanity.com/paper/2406.12975/) (2024)
- [Comparing Hallucination Detection Metrics for Multilingual Generation](https://mlsanity.com/paper/2402.10496/) (2024)
- [LLMs and Memorization: On Quality and Specificity of Copyright Compliance](https://mlsanity.com/paper/2405.18492/) (2024)
- [Do LLMs Know to Respect Copyright Notice?](https://mlsanity.com/paper/2411.01136/) (2024)

## Built on by (most cited first, 2 of 2)

- [Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode](https://mlsanity.com/paper/2602.00388/) (2026)
- [COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling](https://mlsanity.com/paper/2604.20720/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have raised significant concerns regarding the fair use of copyright-protected content. While prior studies have examined the extent to which LLMs reproduce copyrighted materials, they have predominantly focused on English, neglecting multilingual dimensions of copyright protection. In this work, we investigate multilingual biases in LLM copyright protection by addressing two key questions: (1) Do LLMs exhibit bias in protecting copyrighted works across languages? (2) Is it easier to elicit copyrighted content using prompts in specific languages? To explore these questions, we construct a dataset of popular song lyrics in English, French, Chinese, and Korean and systematically probe seven LLMs using prompts in these languages. Our findings reveal significant imbalances in LLMs' handling of copyrighted content, both in terms of the language of the copyrighted material and the language of the prompt. These results highlight the need for further research and development of more robust, language-agnostic copyright protection mechanisms to ensure fair and consistent protection across languages.