LLM s and Copyright Risks: Benchmarks and Mitigation Approaches (2025)

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.

## introduction the landscape of artificial intelligence has been dramatically transformed by the advent of large language models (llms) such as gpt and its successors @xcite @xcite @xcite . these powerful systems have not only revolutionized natural language processing but have also permeated diverse sectors including healthcare @xcite , software development @xcite , finance @xcite , and education @xcite . 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 @xcite . 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. in this tutorial, we will comprehensively review existing paradigms for copyright risk assessment and mitigation in llms, focusing on their contributions to responsible ai development and deployment. we categorize the approaches into probing and benchmarking, influence analysis, unlearning techniques, and finetuning-based behavior regulation. for probing and benchmarking, we will explore methodologies for creating quantitative measures to assess the extent of copyrighted content reproduction in llm outputs. influence analysis will cover the application of influence functions to detect and quantify the impact of potentially copyrighted material in training data. we will then examine machine unlearning techniques as a means to selectively remove knowledge of copyrighted content from trained llms. finally, we will present efficient finetuning and alignment methodologies designed to modify llm behavior with respect to copyright considerations. participants will learn about recent trends and emerging challenges in copyright-aware llm research, as well as resources and tools to implement these techniques. the tutorial aims to prompt thorough discussions regarding the impact of copyright considerations on llm development and the broader implications for ai ethics and governance. ## tutorial outline we will cover four topics on how to measure and mitigate copyright risks of llms. (1) llms copyright risks probing and benchmarking: we will introduce and analyze state-of-the-art techniques for probing llms to identify potential copyright infringements. this section will cover methodologies for creating benchmarks that quantitatively assess the extent of copyrighted content reproduction in llm outputs. (2) influence function for copyright detection in training data: we will examine the application of influence functions, a technique from robust statistics, to detect and quantify the impact of potentially copyrighted material in llm training datasets to model generations. this approach offers a principled way to trace model outputs back to specific training instances. (3) mitigating copyright risks via machine unlearning: this section will discuss cutting-edge machine unlearning techniques as a means to selectively remove knowledge of copyrighted content from trained llms. we will discuss the theoretical foundations of unlearning algorithms and their practical implementation in the context of largescale language models. (4) efficient finetuning and alignment to regulate llms behavior: we will review efficient finetuning methodologies and alignment techniques designed to modify llm behavior with respect to copyright considerations. this includes exploring parameter-efficient tuning methods and reinforcement learning approaches for aligning model outputs with copyright regulations.