This video delves into how Large Language Models (LLMs) store factual information, specifically highlighting the critical role of Multi-Layer Perceptrons (MLPs) within the transformer architecture. It meticulously explains the MLP's internal operations, including up-projection, non-linear activation via ReLU, down-projection, and the use of residual connections, illustrated with a concrete example. The discussion also covers the substantial parameter count of MLPs in models like GPT-3 and introduces the 'superposition' hypothesis, suggesting that high-dimensional spaces allow LLMs to store an exponentially greater number of features, which contributes to their impressive scalability.