Pattern reproduction is very close to speech in my opinion. Formal grammars even have it in the name and approaches like https://news.ycombinator.com/item?id=37

Pattern reproduction is very close to speech in my opinion. Formal grammars even have it in the name and approaches like https://news.ycombinator.com/item?id=37125118 https://news.ycombinator.com/item?id=37125118 show that LLMs are indeed very fit for that purpose. I think I have to walk that claim about math back and try to phrase what I meant differently: LLMs have a hard time with problems that don't translate well into the text space, i.e. abstract problems. Math used to be one of those because early tokenizers were designed just with text in mind and LLMs weren't good enough to overcome those limitations. OpenAI put in a lot of effort into their tokenizers to make GPT3.5 and GPT4 better at math specifically. The second paper you linked is very interesting and I think it supports my original assertion of text space and latent space being close. The first graph shows GPT3.5 doing much better at pattern reproduction and language tasks while humans still hold an advantage in the more abstract tasks like story analogies. Higher order relations being a key thing that's measured maybe makes this task a bit too perfect for arguing my case, but it does show that humans have an advantage in more abstract situations. I think any problem that can be viewed as being mostly a form of translation is a good one for LLMs and if you can express a problem as that, you can get better results. To get back to the main point: latent space and text space, or feature space in general, being close is what I believe causes all of this. Happy to hear counterexamples.