| > You've practically restated the paper's findings! :D The LLM knew nothing about othello; it wasn't shown any rules to be recombined. Literal quote from the paper: "As a first step, we train a language model (a GPT variant we call Othello-GPT) to extend partial game transcripts (a list of moves made by players) with legal moves." And then: "Nonetheless, our model is able to generate legal Othello moves with high accuracy". So: - it knows about the game because it was literally shown the game with only the legal moves - it doesn't produce legal moves all the time (even though it does so with high accuracy) That's why I say "the work done is beyond any shadow of the doubt brilliant". Because this is a definite leap forward from the status quo. However, it doesn't imply that the models can invent/predict/come up with novel ways of doing something. This is still strictly within the realm of "given existing data, give back a statistically relevant response". Could it actually invent Reversi/Othello had it not known about it beforehand? |
It's shown token sequences only. It has no idea they represent a game, or that the game has legal and illegal moves. And more importantly, it has no idea that each token modifies the state of a gameboard, or that simulating how that gameboard changes after every token is the only way to understand the token's grammar. It invents all that.
> Could it actually invent Reversi/Othello had it not known about it beforehand?
You mean, could an LLM invent othello even if its training material made no mention of the game or its rules? Presumptively, of course - why not? Suppose you go make up an arbitrary board game right now. If you then ask ChatGPT-4 to invent a boardgame of its own, nothing excludes the possibility that it will describe a game isomorphic to yours. Obviously the odds are very low, but why imagine that it's not possible?