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it's not about a single point encapsulating a novel, but how sequences of such embeddings can represent complex ideas when processed by the model's layers. eac

it's not about a single point encapsulating a novel, but how sequences of such embeddings can represent complex ideas when processed by the model's layers. each prediction is based on a weighted context of all previous tokens, not just the immediately preceding one.

rollinDyno 2y ago
That weighted context is the 12228 dimensional vector, no? I suppose that when you each element in the vector weighs 16 bits then the space is immense and capable to have a novel in a point.
- causal 2y ago
  But if I understand correctly, GPT-4 reduces that to a 1536-dimensional vector. Roughly 1/8th. It's counterintuitive to me.
- faramarz 2y ago
  GPT-4 is configurable up to 96 layers, each running their own embeddings. I think it was a business choice to afford the compute while they scale.