2 ms·

> I'm not sure I agree that LLMs solve for the statistical mean The good thing is that you don't have to agree with that, as the fundamental technical reality

• wongarsu 2mo ago
  Doesn't the universal approximation theorem apply to typical LLM architectures? LLMs solve for the shape of the input data, as well as they can approximate it in the constraints of their architecture, the optimizer and the amount of compute spent in the training process And where those constraints prevent us from matching the shape there is no guarantee that we match anything like the mean. Though maybe we can agree that that's where the optimizer would tend to steer towards when it can't do any better