Something weird is happening with LLMs and chess

I don't understand why educated people expect that an LLM would be able to play chess at a decent level.

It has no idea about the quality of it's data. "Act like x" prompts are no substitute for actual reasoning and deterministic computation which clearly chess requires.

This is a puzzle given enough training information. LLM can successfully print out the status of the board after the given moves. It can also produce a not-terrible summary of the position and is able to list dangers at least one move ahead. Decent is subjective, but that should beat at least beginners. And the lowest level of stockfish used in the blog post is lowest intermediate.

I don't know really what level we should be thinking of here, but I don't see any reason to dismiss the idea. Also, it really depends on whether you're thinking of the current public implementations of the tech, or the LLM idea in general. If we wanted to get better results, we could feed it way more chess books and past game analysis.

LLMs like GPT aren’t built to play chess, and here’s why: they’re made for handling language, not playing games with strict rules and strategies. Chess engines, like Stockfish, are designed specifically for analyzing board positions and making the best moves, but LLMs don’t even "see" the board. They’re just guessing moves based on text patterns, without understanding the game itself.

Plus, LLMs have limited memory, so they struggle to remember previous moves in a long game. It’s like trying to play blindfolded! They’re great at explaining chess concepts or moves but not actually competing in a match.

> but LLMs don’t even "see" the board

This is a very vague claim, but they can reconstruct the board from the list of moves, which I would say proves this wrong.

> LLMs have limited memory

For the recent models this is not a problem for the chess example. You can feed whole books into them if you want to.

> so they struggle to remember previous moves

Chess is stateless with perfect information. Unless you're going for mind games, you don't need to remember previous moves.

> They’re great at explaining chess concepts or moves but not actually competing in a match.

What's the difference between a great explanation of a move and explaining every possible move then selecting the best one?

Chess is not stateless. En Passant requires last move and castling rights requires nearly all previous moves.

https://adamkarvonen.github.io/machine_learning/2024/01/03/c...

Ok, I did go too far. But castling doesn't require all previous moves - only one bit of information carried over. So in practice that's board + 2 bits per player. (or 1 bit and 2 moves if you want to include a draw)

Castling requires no prior moves by either piece (King or Rook). Move the King once and back early on, and later, although the board looks set for castling, the King may not.

Yes, which means you carry one bit of extra information - "is castling still allowed". The specific moves that resulted in this bit being unset don't matter.

Ok, then for this you need minimum of two bits - one for kingside Rook and one for the queenside Rook, both would be set if you move the King. You also need to count moves since the last exchange or pawn move for the 50 move rule.

Ah, that one's cool - I've got to admit I've never heard of the 50 move rule.