Do Large Language Models learn world models or just surface statistics?
> We found that the trained Othello-GPT usually makes legal moves. The error rate is 0.01%; and for comparison, the untrained Othello-GPT has an error rate of 93.29%. This is much like the observation in our parable that the crow was announcing the next moves.<p>I read the whole article, but I think this statement largely disproves the hypothesis? We only need a single counter example to show that Othello-GPT does not have a systemic understanding of the rules, only a statistical inference of them.<p>A model that "knows" the rules will make no errors, a model that makes any errors does not "know" the rules. Simple as.<p>And I feel this way about much of the article, they state they change intermediate activations and <i>therefore</i> the observed valid results prove that the layers make up a rules engine instead of a statistical engine. And I don't make that leap? Why would that necessarily be the case?<p>Obviously a statistical engine trained on legal moves will mostly produce legal output. A rules engine will always produce legal output.