Limited Memorization

Limited memorization in large language models (LLMs) and other generative AI, such as diffusion models and vision-language models, is a critical research area focusing on how these models unintentionally store and reproduce training data. Current research investigates memorization's extent across various architectures, analyzes its impact on model performance and generalization, and explores mitigation strategies including modifying training objectives and employing parameter-efficient fine-tuning. Understanding and controlling memorization is crucial for addressing privacy concerns, ensuring copyright compliance, and building more trustworthy and reliable AI systems.