Knowledge Memorization

Knowledge memorization in large language models (LLMs) focuses on understanding how these models acquire, store, and retrieve factual information, aiming to improve their accuracy and reliability. Current research investigates scaling laws governing memorization capacity, explores the impact of model architecture and training strategies (like continual pre-training and instruction tuning), and develops benchmarks to rigorously assess different aspects of knowledge recall, including consistency and robustness. These efforts are crucial for advancing LLMs as reliable knowledge sources across diverse domains, impacting fields like healthcare, law, and question answering systems.