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arXiv · 2511.02603

CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

Ehsan Aghazadeh·Ahmad Ghasemi·Hedyeh Beyhaghi·Hossein Pishro-Nik
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Abstract

Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency (Wang et al., 2023) strategy requires a fixed number of calls and fails when the correct answer is infrequent. We introduce Confidence-Guided Early Stopping (CGES), a Bayesian framework that forms posteriors over candidate answers and adaptively halts sampling once one answer accumulates enough posterior mass. We prove guarantees in both an ideal calibrated regime and a realistic noisy-confidence regime under a directional drift condition. Averaged over five reasoning benchmarks, CGES reduces the average number of calls by 58% on average (from 16.0 to 6.7) while matching its accuracy within 0.4 percentage points of self-consistency.

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Ehsan Aghazadeh, Ahmad Ghasemi, Hedyeh Beyhaghi, Hossein Pishro-Nik. 2025-11-04. CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency. https://arxiv.org/abs/2511.02603

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