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by falcor84
680 days ago
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That's not what "hallucination" is. Hallucinations in LLMs are when they unexpectedly and confidently extrapolate outside of their training set when you expected them to generate something interpolated from their training set. In your example that's just a pollution of the training set by spam, but that's not that much of an issue in practice, as AI has been better than humans at classifying spam for over a decade now. |
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If I agree with your definition of hallucinations in the context of LLMs... Then isn't your second paragraph literally just a way to artificially increase the likelihood of them occurring?
You seem to differentiate between a hallucination caused by poisoning the dataset vs a hallucination caused by correct data, but can you honestly make such a distinction considering just how much data goes into these models?