This talk Vishal Misra was an eye-opener to how few shot prompting works under the hood to change output token probability distributions. I forgot LLMs don't produce tokens directly. They produce embeddings from which nearest neighbors tokens are chosen.
youtu.be/sLodkyHlQhY
tl;dr LLMS as Bayesian learning : Given a prompt, looks for something close in training set, then uses prompt for new evidence. Then computes a posterior using this new evidence. This posterior distribution is what is used to generate the new text. (Vishal Misra)
It's so crazy…
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