BARS: Budget-Aware Sequential Sampling with Refusal for Majority Identification under Unknown Distributions

arXiv:2611.14171v1 Announce Type: new Abstract: Repeated sampling with majority selection is an effective means of enforcing self-consistency in large language models. Recently, a variant has also been introduced that terminates early to save costs when the output distribution is favorable. But it still cannot yield a correct confidence estimate from the answer distribution. For this issue, we formalize this as majority identification under an unknown distribution with a budget and a target error rate, and propose BARS, a sequential rule certifying the top-two gap and returning an early stop,

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