Knowledge Storage

Knowledge storage in large language models (LLMs) is a burgeoning research area focused on understanding how these models encode and retrieve factual information. Current investigations explore the localization of knowledge within model architectures, examining the roles of attention mechanisms and MLP weights, and developing efficient caching strategies like RAGCache and FedCache 2.0 to improve knowledge access and reduce computational costs. These efforts aim to improve LLMs' accuracy, efficiency, and robustness, with implications for various applications including personalized federated learning and enhanced retrieval-augmented generation.