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.
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.
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.
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?
“The game is not automatically drawn if a position occurs for the third time – one of the players, on their turn, must claim the draw with the arbiter. The claim must be made either before making the move which will produce the third repetition, or after the opponent has made a move producing a third repetition. By contrast, the fivefold repetition rule requires the arbiter to intervene and declare the game drawn if the same position occurs five times, needing no claim by the players.”