That's the original argument.
> Tuning creates the contextual framework on which test is mapped to a latent space that encodes the meaning and most likely next sequences of text rather than just raw most likely sequence of text as seen in training data
That's different than understanding, or knowing. The encoded meaning is not accessible to the LLM, but the human it's presented to. An LLM cannot know about things it has or has not stored in source data, because it is not actually informed by the information processed. You do need proper cognition to know if information is in source data, because reasoning about information strictly requires interpretation and understanding intent, otherwise it's just data.
It does not have cognition. And yet it can do this. Ergo it does not need cognition to do this.
LLMs have easily demonstrated reasoning capabilities. The encoded meaning is very clearly explored by the model through its tuned framework and I think it’s ridiculous to pretend otherwise.
It’s not stepping through reflection steps in a way that is familiar to humans, but it absolutely is running through semantically defined pattern processing steps. And “known” vs “not known” is one such pattern.
We're arguing about different things, or about different levels of abstraction. Have fun with ChatGPT.
Your argument seems to be that cognition is required to do this perfectly even though things with cognition frequently get this wrong and the bar of the conversation was whether it could be done at all. So I think it seems to be a pretty bad argument.
> Unless you can explain how an actual understanding emerges within an LLM
Tuning creates the contextual framework on which test is mapped to a latent space that encodes the meaning and most likely next sequences of text rather than just raw most likely sequence of text as seen in training data. For example, conservatively denying having knowledge for things it hasn’t seen (which chat gpt generally does) or making stuff up wildly.
> you can't explain how it would answer the question definitely
Of course not. It’s a random behavior. It has plenty of flaws.