[Iulioh]

The point is, your position is against a inherent characteristic of LLMs.

LLMs hallucinate.

That's true and by how they are made it cannot be false.

Anything they generate cannot bw trusted and have to be verified.

They are good at generating fluff but i wouldn't rely on them for anything.

Ask at that temperature glass melts and you will get 5 different answers, noone true.

[owenpalmer]

It got the question correct in 3 trials, with 1 of the trials being the smaller model.

GPT4o

https://chatgpt.com/share/673578e7-e34c-8006-94e5-7e456aca6f...

GPT4o

https://chatgpt.com/share/67357941-0418-8006-a368-7fe8975fbd...

GPT4o-mini

https://chatgpt.com/share/673579b1-00e4-8006-95f1-6bc95b638d...

[Iulioh]

The problem with these answers is that they are right but misleading in a way.

Glass is not a pure element so that temperature is the "production temperature" but as an amorphous material it ""melts"" in the way a plastic material ""melts"" and can be worked at temperature as low as 5-700c.

I feel like without a specification the answer is wrong by omission.

What "melts" means when you are not working with a pure element is pretty messy.

This came out in a discussion for a project with a friend too obsessed with GPT (we needed that second temperature and i was "this can't be right....it's too high")

[mav3ri3k]

Yes. This is funny when I know what is happening and I can "guide" the LLM to the right answer. I feel that is the only correct way to use LLMs and it is very productive. However, for learning, I don't know how anyone can rely on them when we know this happens.