Something weird is happening with LLMs and chess

Maybe that one which plays chess well is calling out to a real chess engine.

this possibility is discussed in the article and deemed unlikely

Note: the possibility is not mentioned in the article but rather in the comments [1]. I had to click a bit to see it.

The fact that the one closed source model is the only one that plays well seems to me like a clear case of the interface doing some of the work. If you ask ChatGPT to count until 10000 (something that most LLMs can't do for known reasons) you get an answer that's clearly pre-programmed. I'm sure the same is happening here (and with many, many other tasks) - the author argues against it by saying "but why isn't it better?", which doesn't seem like the best argument: I can imagine that typical ChatGPT users enjoy the product more if they have a chance to win once in a while.

[1] https://dynomight.substack.com/p/chess/comment/77190852

What do you mean LLMs can't count to 10,000 for known reasons?

Separately, if you are able to show OpenAI is serving pre canned responses in some instances, instead of running inference, you will get a ton of attention if you write it up.

I'm not saying this in an aggro tone, it's a genuinely interesting subject to me because I wrote off LLMs at first because I thought this was going on.* Then I spent the last couple years laughing at myself for thinking that they would do that. Would be some mix of fascinated and horrified to see it come full circle.

* I can't remember, what, exactly, it was far back as 2018. But someone argued that OpenAI was patching in individual answers because scaling was dead and they had no answers, way way before ChatGPT.

When it comes to counting, LLMs have a couple issues.

First, tokenization: the tokenization of 1229 is not guaranteed to be [1,2,2,9] but it could very well be [12,29] and the "+1" operation could easily generate tokens [123,0] depending on frequencies in your corpus. This constant shifting in tokens makes it really hard to learn rules for "+1" ([9,9] +1 is not [9,10]). This is also why LLMs tend to fail at tasks like "how many letters does this word have?": https://news.ycombinator.com/item?id=41058318

Second, you need your network to understand that "+1" is worth learning. Writing "+1" as a combination of sigmoid, products and additions over normalized floating point values (hello loss of precision) is not trivial without degrading a chunk of your network, and what for? After all, math is not in the domain of language and, since we're not training an LMM here, your loss function may miss it entirely.

And finally there's statistics: the three-legged-dog problem is figuring out that a dog has four legs from corpora when no one ever writes "the four-legged dog" because it's obvious, but every reference to an unusual dog will include said description. So if people write "1+1 equals 3" satirically then your network may pick that up as fact. And how often has your network seen the result of "6372 + 1"?

But you don't have to take my word for it - take an open LLM and ask it to generate integers between 7824 and 9954. I'm not optimistic that it will make it through without hallucinations.