This statement on "learning world models" lies between overhyping, nitpicking and wishful thinking. There are many different ways we represent world knowledge, and llms are great in problems that relate with some of them, and horrible at others. For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps. There are problems that grade school children can easily solve with a graphical schema and the most advanced LLMs struggle with.
You can very easily give "evidence" of gpt4 being anywhere between emerging super-intelligence and a naked emperor depending what you ask it to solve. They do not learn models of the world, they learn models of some class of our models of the world, which are very specific and already very restricted in how they represent the world.
> For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps
Of course they are, they haven't been trained on anything spatial, they've only been trained on text that only vaguely describes spatial relations. A world model built from an anemic description of the world will be anemic.
You can very easily give "evidence" of gpt4 being anywhere between emerging super-intelligence and a naked emperor depending what you ask it to solve. They do not learn models of the world, they learn models of some class of our models of the world, which are very specific and already very restricted in how they represent the world.