>is there a theoretical reason why we should expect a large language model to be good at sudokus
Because LLMs have shown the ability to be good at many tasks not directly related to language, and even exhibited some crude "general intelligence" traits.
So, some people would like to find how far this can be pushed, and why it works for e.g. a lot of tasks involving abstract manipulation of symbols and logical analysis, but not for a basic enough and clear goal like solving a simple sudoku.
It's very hard to define what is and is not "related to language" and this is kind of a fundamental question that seemed to get a lot of attention in the 20th century. Maybe these language models can help shine some light on that.
According to OpenAI, GPT-4 scores 4 on AP Calculus BC, 5 on AP Statistics, 4 on AP Chemistry, 4 on AP Physics 2. But is mathematical/logical reasoning largely a language task? I don't really know. I feel pretty confident saying that riding a bike is not a language task, but logical reasoning, I'm not so sure.
You also have to recall that these models were trained on the study materials of all of those tasks. That doesn't cheapen the achievement except to say, it's not "emergent behavior". Probably has half a billion weights dedicated to each of those exams.
Because LLMs have shown the ability to be good at many tasks not directly related to language, and even exhibited some crude "general intelligence" traits.
So, some people would like to find how far this can be pushed, and why it works for e.g. a lot of tasks involving abstract manipulation of symbols and logical analysis, but not for a basic enough and clear goal like solving a simple sudoku.