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SkinnyJoshPeck t1_jdvpkge wrote

Reply to comment by Ok-Hunt-5902 in [D]GPT-4 might be able to tell you if it hallucinated by Cool_Abbreviations_9

but as others are saying, who knows if those confidence scores aren’t also just generated to look like confidence scores. we should ask it for a bunch of confidence scores for sources and see what the actual classification metrics are.. it could just be assuming the further a source is from the top, the less likely it is to be a real source. i don’t see how it could possibly have an understanding that isn’t completely binary since it seems to be generating the fake sources itself.

imo, it’s a bit sketchy if it only identifies its own fake sources with anything less than 100% - it implies basically two things: there is secondary models for true v. false that’s detached from its generative stuff (why wouldn’t it have something that says “this isn’t a great response, maybe i should admit that”); and it seems to have the ability to deceive lol

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Peleton011 t1_jdvtqq0 wrote

Unless I'm wrong somewhere LLMs work with probabilities, they output the most likely response based on training.

They definitely could be able to show you how likely of a response a given paper is, and given that the real papers would be part of the training set answers it's less sure of are going to statistically be less likely to be true.

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RageOnGoneDo t1_jdxm91o wrote

Why are you assuming it's actualyl doing that calculation, though?

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Peleton011 t1_jdxolt1 wrote

I mean, i said LLMs definetely could do that, i never intended to convey that that's what's going on in OPs case or that chatgpt specifically is able to do so.

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RageOnGoneDo t1_jdxoqxf wrote

How, though? How can an LLM do that kind of statistical analysis?

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