I'm on board with a lot of what's in this deck, but I take issue with the argument on slide 9. Roughly, the probability that an LLM-provided answer is fully correct decreases exponentially with the length of the answer. I think that's trivially true, but it's also true for human-provided answers (a full non-fiction book is going to have some errors), so it doesn't really get to the core problem with LLMs specifically.
In much of the rest of the deck, it's just presumed that any variable named x comes from the world in some generic way, which doesn't really distinguish why those are a better basis for knowledge or reasoning than the linguistic inputs to LLMs.
I think we're at the point where people working in these areas need some exposure to the prior work on philosophy of mind and philosophy of language.
In much of the rest of the deck, it's just presumed that any variable named x comes from the world in some generic way, which doesn't really distinguish why those are a better basis for knowledge or reasoning than the linguistic inputs to LLMs.
I think we're at the point where people working in these areas need some exposure to the prior work on philosophy of mind and philosophy of language.