@inproceedings{zhang-etal-2025-llms,
    title = "{LLM}s and Copyright Risks: Benchmarks and Mitigation Approaches",
    author = "Zhang, Denghui  and
      Xu, Zhaozhuo  and
      Zhao, Weijie",
    editor = "Lomeli, Maria  and
      Swayamdipta, Swabha  and
      Zhang, Rui",
    booktitle = "Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-tutorial.7/",
    pages = "44--50",
    ISBN = "979-8-89176-193-3",
    abstract = "Large Language Models (LLMs) have revolutionized natural language processing, but their widespread use has raised significant copyright concerns. This tutorial addresses the complex intersection of LLMs and copyright law, providing researchers and practitioners with essential knowledge and tools to navigate this challenging landscape. The tutorial begins with an overview of relevant copyright principles and their application to AI, followed by an examination of specific copyright issues in LLM development and deployment. A key focus will be on technical approaches to copyright risk assessment and mitigation in LLMs. We will introduce benchmarks for evaluating copyright-related risks, including memorization detection and probing techniques. The tutorial will then cover practical mitigation strategies, such as machine unlearning, efficient fine-tuning methods, and alignment approaches to reduce copyright infringement risks. Ethical considerations and future directions in copyright-aware AI development will also be discussed."
}