Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?

MA Panaitescu-Liess, Z Che, B An, Y Xu… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) have demonstrated impressive capabilities in generating
diverse and contextually rich text. However, concerns regarding copyright infringement arise …

Tackling GenAI Copyright Issues: Originality Estimation and Genericization

H Chiba-Okabe, WJ Su - arXiv preprint arXiv:2406.03341, 2024 - arxiv.org
The rapid progress of generative AI technology has sparked significant copyright concerns,
leading to numerous lawsuits filed against AI developers. While some studies explore …

Data Attribution for Text-to-Image Models by Unlearning Synthesized Images

SY Wang, A Hertzmann, AA Efros, JY Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
The goal of data attribution for text-to-image models is to identify the training images that
most influence the generation of a new image. We can define" influence" by saying that, for a …

Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations

C Chen, Z Liu, W Jiang, GS Qi, KKY Lam - arXiv preprint arXiv:2408.12935, 2024 - arxiv.org
AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

Probabilistic Analysis of Copyright Disputes and Generative AI Safety

H Chiba-Okabe - arXiv preprint arXiv:2410.00475, 2024 - arxiv.org
This paper presents a probabilistic approach to analyzing copyright infringement disputes by
formalizing relevant judicial principles within a coherent framework based on the random …