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by Infinity315
658 days ago
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> This particular hypothesis is obviously a testable one: someone could simply follow the proposed steps from the hypothesis (e.g. finetune a model on their incident response data), and see if it works. Are you saying that if someone finetunes a current SOTA LLM with incident response data and demonstrates that it doesn't work that you'll say that LLMs are infeasible for this application? That would invalidate the hypothesis: "X application can be done on current LLMs." Such a test could never invalidate the the hypothesis: "X application can (eventually) be done on LLMs." If it's the former hypothesis you were asserting, then yes I agree that it is testable, but I'm fairly confident you were asserting the latter. Earlier I had asked you: "I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here?" And you have yet to provide a response. |
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Proving that LLMs can never do this would require extremely rigorous theoretical evaluation that even top ML labs are currently unable to do, given the problem of interpretability. In general proving a negative is typically harder than a positive, since a single experiment succeeding proves a positive, but a single experiment failing does not prove a negative; generally science does not demand that scientists attempt to prove a negative when running experiments, or else nearly every drug trial, for example, would be impossible to perform. Complaining that you have staked out a very difficult to defend position — that it's impossible for LLMs to generate good incident reports — does not mean your ideological opponents, who have simpler positions, must do your proof work for you.