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When LLMs hallucinate and I point it out they usually get it right the second go. I guess if you could get it to use its own output as input that would be simi
by dools 2y ago
When LLMs hallucinate and I point it out they usually get it right the second go.
I guess if you could get it to use its own output as input that would be similar, but I think that it’s the additional information in the follow up that makes the difference.
Like it can write code, I run the code, there is an error, I paste the error and it corrects the code.
So WTF couldn’t it just produce the correct code the first time? Probably for the same reason I can’t.
The additional information of the error causes me to re evaluate the code in a different way than when I first wrote it.
As such I don’t think you can just tell it “do better the first time” any more than you can tell a human to “do better the first time”.
We and they both get additional information from failing.
- sebstefan 2y ago>So WTF couldn’t it just produce the correct code the first time? Probably for the same reason I can’t. We still know the gist of how they work and it's not like you do, and you can see when they fail that they don't fail for the same reasons either I've seen GPT 3.5 fail to answer some questions correctly and subsequently (in another tab) get it right/choose not to answer if you just add the line "don't hallucinate" I like the article's idea for why that is >Presumably, "retrieving from memory" and "improvising an answer" are two different model behaviors, which use different internal mechanisms. Indeed, we can probe model inner layers and infer if it is "lying"1 or if "the question is unanswerable"2. These are very much related to "hallucinations".