Regarding medical information: medical professionals in the US, including your doctor, use uptodate.com, which is basically a medical encyclopedia that is regularly updated by experts in their field. While it's very expensive to get a year long subscription, a week long subscription (for non medical professionals) is only around $20 and you can look up anything you want.
My example is asking for way less than what you're asking for.
Here is something I do not see with reasonable humans who are cooperative:
Me: "hey friend with whom I have plans to get dinner, what are you thinking of eating?"
Friend: "fried chicken?"
Me: "I'm vegetarian"
Friend: "steak?"
Note that this is in the context of four turns of a single conversation. I don't expect people to remember stuff across conversations or to change their habits or personalities.
One common kind of interaction I have with chatgpt (pro):
1. I ask for something
2. Chatgpt suggests something that doesn't actually fulfill my request
3. I tell it how its suggestion does not satisfy my request.
4. It gives me the same suggestion as before, or a similar suggestion with the same issue.
Chatgpt is pretty bad at "don't keep doing the thing I literally just asked you not to do" but most humans are pretty good at that, assuming they are reasonable and cooperative.
+1, I am also big user of PGMs, and also a big user of transformers, and I don't know what the parent comment talking about, beyond that for e.g. LLMs, sampling the next token can be thought of as sampling from a conditional distribution (of the next token, given previous tokens). However, this connection of using transformers to sample from conditional distributions is about autoregressive generation and training using next-token prediction loss, not about the transformer architecture itself, which mostly seems to be good because it is expressive and scalable (i.e. can be hardware-optimized).
Source: I am a PhD student, this is kinda my wheelhouse