Large Language Models (LLMs) have transformed machine learning but raised
significant legal concerns due to their potential to produce text that
infringes on copyrights, resulting in several high-profile lawsuits. The legal
landscape is struggling to keep pace with these rapid advancements, with
ongoing debates about whether generated text might plagiarize copyrighted
materials. Current LLMs may infringe on copyrights or overly restrict
non-copyrighted texts, leading to these challenges: (i) the need for a
comprehensive evaluation benchmark to assess copyright compliance from multiple
aspects; (ii) evaluating robustness against safeguard bypassing attacks; and
(iii) developing effective defenses targeted against the generation of
copyrighted text. To tackle these challenges, we introduce a curated dataset to
evaluate methods, test attack strategies, and propose lightweight, real-time
defenses to prevent the generation of copyrighted text, ensuring the safe and
lawful use of LLMs. Our experiments demonstrate that current LLMs frequently
output copyrighted text, and that jailbreaking attacks can significantly
increase the volume of copyrighted output. Our proposed defense mechanisms
significantly reduce the volume of copyrighted text generated by LLMs by
effectively refusing malicious requests. Code is publicly available at
this https URL

当前大型语言模型存在版权侵权问题，相关挑战包括版权合规评估、鲁棒性防御以及生成版权文本的有效防御机制。本文介绍了一个数据集用于评估方法、测试攻击策略，并提出了轻量级、实时的防御机制以确保大型语言模型的安全合法使用。实验证明，当前大型语言模型存在生成版权文本的问题，而越狱攻击会显著增加生成的版权文本量。我们提出的防御机制通过有效拒绝恶意请求，显著减少了大型语言模型生成的版权文本量。代码公开可用于该链接网址。