Prévision communautaire actuelle
Non 86.9%
Prévisionnistes
54
Type de question
binary
Méthodologie
Play-money forecasting platform
Type de source
Prévision
Données du marché
Mis à jour il y a 16 heures
Obsolète27 févr. 23, 23:31 UTC2 janv. 27, 05:59 UTC
Tendances
Règles
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:
