>1 illegal move in 1 million or 100 million or any other sample size you want to choose means it doesn’t understand chess
Highly rated chess players make illegal moves. It's rare but it happens. They don't understand chess ?
Highly rated chess players make illegal moves. It's rare but it happens. They don't understand chess ?
Humans with correct models may nevertheless make errors in rule applications. Machines are good at applying rules, so when they fail to apply rules correctly, it means they have incorrect, incomplete, or totally absent models.
Without using a word like “understands” it seems clear that the same apparent mistake has different causes.. and model errors are very different from model-application errors. In a math or physics class this is roughly the difference between carry-the-one arithmetic errors vs using an equation from a completely wrong domain. The word “understands” is loaded in discussion of LLMs, but everyone knows which mistake is going to get partial credit vs zero credit on an exam.
>Machines are good at applying rules, so when they fail to apply rules correctly, it means they have incorrect or incomplete models.
I don't know why people continue to force the wrong abstraction. LLMs do not work like 'machines'. They don't 'follow rules' the way we understand normal machines to 'follow rules'.
>so when they fail to apply rules correctly, it means they have incorrect or incomplete models.
Everyone has incomplete or incorrect models. It doesn't mean we always say they don't understand. Nobody says Newton didn't understand gravity.
>Without using a word like “understands” it seems clear that the same apparent mistake has different causes.. and model errors are very different from model-application errors.
It's not very apparent no. You've just decided it has different causes because of preconceived notions on how you think all machines must operate in all configurations.
LLMs are not the logic automatons in science fiction. They don't behave or act like normal machines in any way. The internals run some computations to make predictions but so does your nervous system. Computation is substrate-independent.
I don't even know how you can make this distinction without seeing what sort of illegal moves it makes. If it makes the sort high rated players make then what ?
- 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.
> 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.
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.
That's assuming that, somehow, a LLM is a machine. Why would you think that?
I think we are discussing whether LLMs can emulate chess playing machines, regardless of whether they are actually literally composed of a flock of stochastic parrots..
> Machines are good at applying rules, so when they fail to apply rules correctly, it means they have incorrect, incomplete, or totally absent models.
If this line of reasoning applies to machines, but LLMs aren't machines, how can you derive any of these claims?
"A implies B" may be right, but you must first demonstrate A before reaching conclusion B..
> I think we are discussing whether LLMs can emulate chess playing machines
That is incorrect. We're discussing whether LLMs can play chess. Unless you think that human players also emulate chess playing machines?