Current community forecast
No 86.9%
Forecasters
54
Question type
binary
Methodology
Play-money forecasting platform
Source type
Forecast
Market data
Updated 17 minutes ago
Feb 27, 23, 11:31 PM UTCJan 2, 27, 5:59 AM UTC
Trends
Rules
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:
