4 ms·
> Except they can play chess better than 90% of humans... Just by luck of course..No understanding. I've been thinking about this and I think there is a simple
by lewhoo 3y ago
> Except they can play chess better than 90% of humans... Just by luck of course..No understanding.
I've been thinking about this and I think there is a simple trick to make LLM's play chess well. Any chess position can be represented by a string, like so:
rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1 (Forsyth–Edwards Notation)
If we think of a chess game as a sentence or a sequence of such strings or tokens, then after feeding tons of those to the model it is reasonable to suspect it will do what it does best, that is predict the next token. There is no spacial awareness or anything beyond that going on I think.
- xrisk 3y agoThere are far too many possible positions aka tokens for this to be feasible.
- lewhoo 3y agoSame could be said about language but here we are. There are far more possible combinations of words in any given language than there are positions in chess for a typical length of a sentence I suspect.
- xrisk 3y agoDefinitely, but treating fen positions as tokens isn’t going to work, you need something smaller, something you can decompose into. What that is I don’t know.
- lewhoo 3y agoWell, every character of the string can be a token. From a transformer's perspective it shouldn't matter if it is Chinese, English or "chess" language.
- johnthewise 3y agoYou are right, but evidence of LLMs working well with language is not a sign that they might be memorizing Chess. But rather opposite, infeasability of memorization of chess and yet LLMs still doing well on them might be a sign that LLMs could be doing the same 'thing' with language.
- Jensson 3y agoLLM are not doing well in chess. They do well when people play standard openings, but play against the LLM using an inexperienced player and the LLM will quickly start making illegal moves and be unable to play. There are many millions of pro chess games recorded on the internet, enough to cover all the openings and plenty of ways to go from there with some basic heuristics, but the more you diverge from those the more the LLM will break down since it doesn't understand the rules, it just memorized a lot of openings and some really primitive patterns to follow from there.
- golol 3y agoThis is not true anymore, see this tweet. https://twitter.com/GrantSlatton/status/1703913578036904431 https://twitter.com/GrantSlatton/status/1703913578036904431 I have tested it myself.
- johnthewise 3y agoIt's not a trick. Every information can be represented by sequence of bits or strings. It's not the surprising part. It's just that its a telling sign that through compression understanding of the game emerges because: 1- unlike language where you can make an argument that maybe model had seen this example in the training set and just parroting it back, you can't really memorize the possible states of chess and thus you can be reasonably sure that any game or state it is in, must be OOD. 2- If the system still plays well with OOD examples, it must be gaining understanding of the game itself. Also check out the Othello paper, where they show the evidence for an LLM encoding the game state in its neurons.
- golol 3y agoThe simple trick has already been discovered See this tweet. https://twitter.com/GrantSlatton/status/1703913578036904431 https://twitter.com/GrantSlatton/status/1703913578036904431 I have tested it myself.