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arXiv · 2403.09849

Self-Consistency Boosts Calibration for Math Reasoning

Ante Wang·Linfeng Song·Ye Tian·Baolin Peng·Lifeng Jin·Haitao Mi·Jinsong Su·Dong Yu
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Abstract

Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development. We design three off-the-shelf calibration methods based on self-consistency (Wang et al., 2022) for math reasoning tasks. Evaluation on two popular benchmarks (GSM8K and MathQA) using strong open-source LLMs (Mistral and LLaMA2), our methods better bridge model confidence and accuracy than existing methods based on p(True) (Kadavath et al., 2022) or logit (Kadavath et al., 2022).

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Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu. 2024-03-14. Self-Consistency Boosts Calibration for Math Reasoning. https://arxiv.org/abs/2403.09849

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