[greatgib]

A real debate is possible on the subject but this blog post worth nothing on the subject.

From my side, I don't really know if what does LLM is thinking, but what amaze me is that: - It is clear to me the way the LLM operate that things are generated token after token, without really a pre-existing plan on what comes next. So, more like a probabilistic repeating machine. - But it the same time, I can see in action LLM capable to create things or reply to questions that clearly does not exist in the training corpus. So it shows a behavior that is similar to thinking to complete tasks.

For example, let's suppose you give him specific tools to your own custom API, you ask him to do a task, and we can observe that it is capable of mixing multiple calls and combination of the tools results to achieve a given purpose.

Otherwise, when you ask LLM to do math operations like 3123454*2030+500 and it is capable to give the good reply (not all the cases but sometimes). Where, despite the huge size of the corpus, there is not exactly all the operations that are exactly available in the corpus for sure.

So, my best guess is that a lot of things in our world are based on "semantic" patterns that we don't know. Especially for math and logic that are bound to the language. To me it is similar to the mentral trick used by "fast calculator".