alt Hacker NewsThis perspective is a useful source of intuition against the “LLMs can’t have new ideas, they’re just next-token-predictors” style arguments. What if you shift your perspective to thinking of training as optimization over a vast parametrized family of compression algorithms? Well, it suddenly looks a lot more plausible that “new” “ideas” can emerge from that process!
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Incidentally, the relationship is bidirectional. You can try it out just for fun. zstd is a pretty crappy language model :)
There's another element to this that I almost never see discussed. Ideas are not facts. Neither LLMs nor humans can generate new knowledge, as opposed to ideas, by thinking alone. Physical investigation and experimentation is necessary.
The exception being pure mathematics since it exists solely in the realm of ideas. I'm willing to call that knowledge, but it's still a distinction, the old analytic/synthetic dichotomy of Kant.
> it suddenly looks a lot more plausible that “new” “ideas” can emerge from that process
This is not intuitive to me. It seems like a "new idea" is something that (almost by definition) isn't in the training set. Can you elaborate a bit?
Edit: but perhaps a good model could arise from training, which would be a good idea in the sense that parsimonious ideas are good scientific ideas.