Rethinking Literary Plagiarism in LLMs through the Lens of Copyright Laws

Huachen Tan, Moming Duan, Duo Liu, HaojieLu, YuexinMu, Longyi Zhou, Ao Ren, Yujuan Tan, Kan Zhong

Keywords: Responsible AI, Generative AI, Copyright Violation, Fair Use

Verify Author List: I have double-checked the author list and understand that additions and removals will not be allowed after the submission deadline.

TL;DR: This paper rethinks literary plagiarism in LLMs through the lens of copyright laws and proposes an LLM-based method for determining plot plagiarism.

Abstract: The swift advancement of Generative Artificial Intelligence (AI) has outstripped the development of corresponding laws and regulations, highlighting books' copyright infringement as a significant public concern and sparking numerous legal disputes. Although fair use doctrine exemption for using copyrighted materials in training datasets without the copyright holder's permission, content generated by such AI systems may still violate copyright laws. Previous research on copyright infringement has primarily focused on character-level analysis, which is narrower in scope compared to the comprehensive requirements of copyright law. To address this challenge, we developed a LLM-based similarity measurement mechanism. We guided the generative AI to produce relevant book content by employing carefully crafted prompts. Subsequently, we created datasets by comparing this generated content with the original texts from famous books. We conducted various experiments, including various similarity detection techniques and plot plagiarism detection. The experimental results show that the AI-generated content (AIGC) is 78.72% similar to the original text, confirming that generative AI has the potential to infringe upon copyrights. Moreover, our study examines copyright infringement issues related to the content generated by generative AI and other domains such as code, images, and licensing. Our research will provide valuable insights for refining laws and regulations about generative AI.

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

Paper Checklist Guidelines: I certify that all co-authors of this work have read and commit to adhering to the guidelines in Call for Papers.

Student Author: Yes

Submission Number: 302