Nice work. But I wonder why people are so surprised that these models and GPT would learn a model of the world. Of course they learn a model of the world. Even the skip-gram and CBOW word vectors people trained ages ago modelled the world, in the sense that for example named entities in vector space would be highly correlated with actual spatial/geographical maps. It should be 100% assumed that these models which have many orders of magnitude more parameters are learning much more sophisticated models of the world. What that tells us about their "intellige... (read more)
I tried to be explicit in the post that I don't personally care all that much about the world model angle - Othello-GPT clearly does form a world model, it's very clear evidence that this is possible. Whether it happens in practice is a whole other question, but it clearly does happen a bit.
I think this undersells it. World models are fundamentally different from surface level statistics, I would argue - a world model is an actual algorithm, with causal links and moving parts. Analogous to how an induction head is a real algorithm (given a token A, search the context for previous occurences of A, and predict that the next token then will come next now), while something that memorises a ton of bigrams such that it can predict B after A is not.
Nice work. But I wonder why people are so surprised that these models and GPT would learn a model of the world. Of course they learn a model of the world. Even the skip-gram and CBOW word vectors people trained ages ago modelled the world, in the sense that for example named entities in vector space would be highly correlated with actual spatial/geographical maps. It should be 100% assumed that these models which have many orders of magnitude more parameters are learning much more sophisticated models of the world. What that tells us about their "intellige... (read more)