If you are using the formal definition of generalization in a machine learning context, then you completely misrepresented Chollet's claims. He doesn't say much about generalization in the sense of in-distribution, unseen data. Any AI algorithm worth a damn can do that to some degree. His argument is about transfer learning, which is simply a more robust form of generalization to out-of-distribution data. A network trained on Go cannot generalize to translation and vice versa.

Maybe you should stick to a single definition of "generalization" and make that definition clear before you accuse people of needing to read ML basics.

I was replying to a claim that LLMs "can’t generalize" at all, and I showed they do within their domain. No I haven't completely misrepresented the claims. Chollet is just setting a high bar for generalization.