6 ms·
>People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist Some limitat
by coldtea 6mo ago
>People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist
Some limitations are not rigorously demonstrated to be fundamental, but continuously present from the first early LLMs yes. Shouldn't the burden of proof be on those who say it can be done?
And some limitations are fundamental, and have been rigorously demonstrated, e.g.:
https://arxiv.org/abs/2401.11817?utm_source=chatgpt.com https://arxiv.org/abs/2401.11817?utm_source=chatgpt.com
- p-e-w 6mo agoThat paper’s abstract doesn’t carry its title, to put it mildly.
- coldtea 6mo agoWhat part of "Specifically, we define a formal world where hallucination is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all the computable functions and will therefore inevitably hallucinate if used as general problem solvers. " doesn't carry the title, to ask mildly?
- p-e-w 6mo agoI don’t agree with that definition of “hallucination”, for starters.
- MarkusQ 6mo agoSo substitute another phrase, if you prefer. It doesn't change the logic. "Specifically, we define a formal world where bungling is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all the computable functions and will therefore inevitably bungle if used as general problem solvers."
- red75prime 6mo agoTheir diagonalization argument applies to any system that uses finite training data. Calling such a system "LLM" is an (unintentional) red herring.
- MarkusQ 6mo agoYeah. IMHO this is the more serious objection.
- gus_massa 6mo ago[dead]
- red75prime 6mo agoAs with all the works that use too broad a definition of an LLM they prove too much. This work defines an "LLM" as a computable function obtained by applying a finite number of steps of a generic algorithm to an initial computable function. What they really prove is that it's impossible to extrapolate unconstrained non-continuous function from a finite subset of its values. Good for them, I guess. It's like saying that the no free lunch theorems proves that LLMs can't be the best optimizers, while it proves (roughly) that the best optimizers don't exists. That is, even people aren't the best optimizers, but we manage somehow, so LLMs can too.