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Comment by Maxatar

7 hours ago

>There is no “oops, I wrote ‘th’ but I should have written ‘tw’ so I guess I’m stuck writing three instead of tween”.

You're mixing up two claims here, and only one of these is kind of true. Yes LLMs do internally plan ahead in a way that is emergent rather than strictly part of their architecture, so that part of your claim is true. The way you word it by saying they are "coalescing the probabilities of a range of tokens at a time" is poetic sounding jibberish though. What's actually happening is one distribution output for the next token computed from a hidden state that implicitly encodes where the text headed.

Your claim that if an LLM does happen to pick a token "th" instead of "tw", then the LLM isn't stuck with that decision is entirely false for autoregressive LLMs which is what all of the frontier models are. Whatever an LLM picks as its output token is final, it has no ability to undo that token selection and it must continue on the basis of that choice. It can't go back on that decision and revise the output.

If you're interested in this, Anthropic has a summary of a very technical paper on this topic that mostly deals with this issue with respect to poetry:

https://www.anthropic.com/research/natural-language-autoenco...

Reasoning tokens are a way to escape autoregressive woes. The model can generate a draft, then ponder on it, and use this to generate a final version

What a crazy link:

  So we train a second copy of Claude to work backwards—reconstruct the original activation from the text explanation. We consider an explanation to be good if it leads to an accurate reconstruction. We then train Claude to produce better explanations according to this definition using standard AI training techniques.

Incentives to train a pathological liar. There's no baseline so can only catch out the worst of the lies/errors. Anything (including fabrications) that passes our filters is reinforced?