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Why language models hallucinate

(openai.com)
277 points simianwords | 2 comments | 06 Sep 25 07:41 UTC | HN request time: 0s | source
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roxolotl ◴[06 Sep 25 13:09 UTC] No.45148981[source]▶
This seems inherently false to me. Or at least partly false. It’s reasonable to say LLMs hallucinate because they aren’t trained to say they don’t have a statistically significant answer. But there is no knowledge of correct vs incorrect in these systems. It’s all statistics so what OpenAI is describing sounds like a reasonable way to reduce hallucinations but not a way to eliminate them nor the root cause.
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1. goalieca ◴[06 Sep 25 13:18 UTC] No.45149040[source]▶
> It’s reasonable to say LLMs hallucinate because they aren’t trained to say they don’t have a statistically significant answer.

I’ve not seen anyone intuitively explain parameters for a real scale model.. perhaps because it’s all just thousand dimensional nonsense.

Statistics is a funny thing too. Pretty much everyone has seen how trend lines don’t always extrapolate very well.

I think OpenAI is biased to thinking that adding more parameters and training better will fix all ills. In a handwaving way, you can see this like adding more degrees to the polynomial when you curve fit on a spreadsheet. With enough parameters you can perfectly fit any dataset. That all works until you run across new inputs that are unlike training data.

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2. utyop22 ◴[06 Sep 25 21:51 UTC] No.45153203[source]▶
"I think OpenAI is biased to thinking that adding more parameters and training better will fix all ills."

Their whole existence depends on this happening. Else they go bust.