[aithrowawaycomm]

In theory transformers are Turing-complete and LLMs can do anything computable. The more down-to-earth argument is that transformer LLMs aren't able to correct errors in a systematic way like Lecun is describing: it's task-specific "whack-a-mole," involving either tailored synthetic data or expensive RLHF.

In particular, if you train an LLM to do Task A and Task B with acceptable accuracy, that does not guarantee it can combine the tasks in a common-sense way. "For each step of A, do B on the intermediate results" is a whole new Task C that likely needs to be fine-tuned. (This one actually does have some theoretical evidence coming from computational complexity, and it was the first thing I noticed in 2023 when testing chain-of-thought prompting. It's not that the LLM can't do Task C, it just takes extra training.)