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Verouderd27 feb 23, 23:31 UTC2 jan 27, 05:59 UTC
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Current large language models (LLMs) are capable in many rich language tasks, but remain prone to a failure mode known as hallucination: models confidently output false "facts" as though they were true.
- One view might be that this is an inherent feature of LLMs based on the current architecture of next-token prediction, since the model has no (direct) notion of overconfidence.
- Such a view might not expect scaling current models to significantly reduce hallucination.
- Another perspective might expect larger models to develop emergent capabilities which reduce or eliminate hallucination.
- If a knowledgeable human would respond to a hallucination-inducing question with something like "the question does not make sense", we might expect a capable language model to learn similar patterns.
- This market resolves to TRUE if I am shown:
