LLMs produce inconsistent probabilities: P(rain) + P(no rain) don't sum to one
soumitrashukla9 · x · 2026-09-05
Suproteem Sarkar gives a concrete illustration of LLM calibration problems: ask a model the probability of rain tomorrow and you may get P(rain)=0.7, then ask the probability it won't rain and get 0.2 — the two never sum to one, violating basic probability axioms. The work is praised as some of the most interesting AI+economics research, examining consistency of model-generated probabilities.
Related event: LLMs give incoherent probabilities: P(rain) + P(no rain) ≠ 1(2 posts)→
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