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So why then do we stop training LLMs and keep them stored at a specific state? Is it perhaps because the results become terrible and LLMs have a delicate optima
by datsci_est_2015 7mo ago
So why then do we stop training LLMs and keep them stored at a specific state? Is it perhaps because the results become terrible and LLMs have a delicate optimal state for general use? This sounds like an even worse case for a model of intelligence.
- stavros 7mo agoNope, it's not that, but it's nice of you to offer a straw man. Makes the argument flow better.
- datsci_est_2015 7mo agoNot entirely a straw man. What is the purpose of storing and retrieving LLMs at a fixed state if not to guarantee a specific performance? Wouldn’t a strong model of intelligence be capable of, to extend your analogy, running without having its hippocampus lobotomized? Given the precariousness of managing LLM context windows, I don’t think it’s particularly unfair to assume that LLMs that learn without limit become very unstable. To steelman, if it’s possible, it may be prohibitively expensive. But somehow I doubt it’s possible.
- stavros 7mo agoIt is, indeed, prohibitively expensive. But it's not impossible. The proof is in the fact that you can fine-tune LLMs.
- deleted 7mo ago[deleted]