The landscape of artificial intelligence has been dramatically transformed by the advent of Large Language Models (LLMs) such as GPT and its successors. These powerful systems have not only revolutionized natural language processing but have also permeated diverse sectors including healthcare, software development, finance, and education. While LLMs have unlocked unprecedented capabilities in text generation and analysis, they have simultaneously given rise to complex legal and ethical challenges, particularly in the realm of copyright law. The ability of these models to produce human-like text has blurred the boundaries between original creation and potential copyright infringement, as evidenced by recent New York Times legal actions against AI company (Microsoft). This tutorial aims to navigate this intricate terrain, providing a comprehensive exploration of the copyright issues surrounding LLMs and equipping participants with the knowledge and tools to address these challenges.
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@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://aclanthology.org/2025.naacl-tutorial.7/", doi = "10.18653/v1/2025.naacl-tutorial.7", pages = "44--50", ISBN = "979-8-89176-193-3" } @inproceedings{xu-etal-2024-llms, title = "Do {LLM}s Know to Respect Copyright Notice?", author = "Xu, Jialiang and Li, Shenglan and Xu, Zhaozhuo and Zhang, Denghui", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1147/", doi = "10.18653/v1/2024.emnlp-main.1147", pages = "20604--20619" }