Verbatim Memorization

Verbatim memorization, the ability of large language models (LLMs) and diffusion models to reproduce training data verbatim, is a significant concern due to privacy and copyright implications. Current research focuses on detecting and mitigating this memorization, exploring methods like unlearning, fine-tuning, and regularization across various architectures including GPT-Neo and Stable Diffusion. The effectiveness of these mitigation techniques remains a key area of investigation, with studies highlighting the challenge of eliminating memorization without substantially impacting model performance. This research is crucial for responsible development and deployment of these powerful models, ensuring ethical and legal compliance.