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I can’t tell if you are saying the distinction between model errors and model-application errors doesn’t exist or doesn’t matter or doesn’t apply here.

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og_kalu 3 days ago | parent [-]

I'm saying:

- Generally, we do not say someone does not understand just because of a model error. The model error has to be sufficiently large or the model sufficiently narrow. No-one says Newton didn't understand gravity just because his model has an error in it but we might say he didn't understand some aspects of it.

- You are saying the LLM is making a model error (rather than an an application error) only because of preconceived notions of how 'machines' must behave, not on any rigorous examination.

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photonthug 3 days ago | parent [-]

Suppose you're right, the internal model of game rules is perfect but the application of the model for next-move is imperfect. Unless we can actually separate the two, does it matter? Functionally I mean, not philosophically. If the model was correct, maybe we could get a useful version of it out by asking it to write a chess engine instead of act as a chess engine. But when the prolog code for that is as incorrect as the illegal chess move was, will you say again that the model is correct, but the usage of it resulted merely resulted in minor errors?

> You are saying the LLM is making a model error (rather than an an application error) only because of preconceived notions of how 'machines' must behave, not on any rigorous examination.

Here's an anecdotal examination. After much talk about LLMs and chess, and math, and formal logic here's the state of the art, simplified from dialog with gpt today:

> blue is red and red is blue. what color is the sky? >> <blah blah, restates premise, correctly answer "red">

At this point fans rejoice, saying it understands hypotheticals and logic. Dialogue continues..

> name one red thing >> <blah blah, restates premise, incorrectly offers "strawberries are red">

At this point detractors rejoice, declare that it doesn't understand. Now the conversation devolves into semantics or technicalities about prompt-hacks, training data, weights. Whatever. We don't need chess. Just look it, it's broken as hell. Discussing whether the error is human-equivalent isn't the point either. It's broken! A partially broken process is no solid foundation to build others on. And while there are some exceptions, an unreliable tool/agent is often worse than none at all.

	▲	og_kalu 3 days ago \| parent [-]
		>It's broken! A partially broken process is no solid foundation to build others on. And while there are some exceptions, an unreliable tool/agent is often worse than none at all. Are humans broken ? Because our reasoning is a very broken process. You say it's no solid foundation ? Take a look around you. This broken processor is the foundation of society and the conveniences you take for granted. The vast vast majority of human history, there wasn't anything even remotely resembling a non-broken general reasoner. And you know the funny thing ? There still isn't. When people like you say LLMs don't reason, they hold them to a standard that doesn't exist. Where is this non-broken general reasoner in anywhere but fiction and your own imagination? >And while there are some exceptions, an unreliable tool/agent is often worse than none at all. Since you are clearly meaning unreliable to be 'makes no mistake/is not broken' then no human is a reliable agent. Clearly, the real exception is when an unreliable agent is worse than nothing at all.