Part 6/8:
The total number of parameters in an LLM like GPT-3 is staggering—standing at approximately 175 billion. Notably, about two-thirds of these reside within the MLP blocks. These parameters—comprising both weights and biases—crucially shape the model's ability to model complex relationships between words and their meanings.
A fascinating aspect discussed suggests that the neurons within these networks often do not represent clear, singular facts as initially presumed. Instead, they might embody a combination of features in a "superposition" state, allowing the model to represent more information concurrently than there are individual dimensions in its parameter space.