this post was submitted on 04 Apr 2025
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How is this surprising, like, at all? LLMs predict only a single token at a time for their output, but to get the best results, of course it makes absolute sense to internally think ahead, come up with the full sentence you're gonna say, and then just output the next token necessary to continue that sentence. It's going to re-do that process for every single token which wastes a lot of energy, but for the quality of the results this is the best approach you can take, and that's something I felt was kinda obvious these models must be doing on one level or another.
I'd be interested to see if there are massive potentials for efficiency improvements by making the model able to access and reuse the "thinking" they have already done for previous tokens
well because when you say things like "it plans ahead" or "our method is inspired by brain scanners" etc it makes a connection between AI and real thinking and generates hype.
I wanted to say exactly this. If you’ve ever written rap/freestyled then this is how it’s generally done.
You write a line to start with
“I’m an AI and I think differentially”
Then you choose a few words that fit the first line as best as you could: (here the last word was “differentially”)
Then you try them out and see what clever shit you could come up with:
Then you sort them in a way that makes sense and come up with word play/schemes to embed it between, break up the rhyme scheme if you want (AABB, ABAB, AABA, etc)
You get the idea.
Edit: in hindsight, that was a horrendous example. I suck at this, colossally.