Self-Consistency Boosts Calibration for Math Reasoning

• URL: http://arxiv.org/abs/2403.09849v1
• Date: Thu, 14 Mar 2024 20:17:10 GMT
• Title: Self-Consistency Boosts Calibration for Math Reasoning
• Authors: Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Lifeng Jin, Haitao Mi, Jinsong Su, Dong Yu,
• Abstract summary: We design three off-the-shelf calibration methods based on self-consistency for math reasoning tasks. Our methods better bridge model confidence and accuracy than existing methods based on p(True) or logit.
• Score: 69.82896431282927
• License: http://creativecommons.org/licenses/by/4.0/
• 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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