[coldtea]

>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.

[objektif]

What tasks would you say LLMs are good at that are not related to language?

[skwirl]

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.

[thumbuddy]

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.

[objektif]

Exactly what I was trying to imply. Very difficult to classify what is not relevant to language.