相信你的模型：分布引导的置信度校准

摘要

随着测试时扩展技术的发展，大型推理模型展现出卓越性能，该技术通过生成多个候选响应并选择最可靠答案来提升预测准确率。尽管已有研究分析指出，置信度等内部模型信号可部分指示响应正确性，并与准确率存在分布相关性，但此类分布信息尚未被充分用于指导答案选择。基于此，我们提出DistriVoting方法，在投票过程中将分布先验作为置信度之外的补充信号。具体而言，我们的方法（1）首先利用高斯混合模型将混合置信度分布分解为正负两个分量，（2）随后基于这两个分量中的正负样本应用拒绝过滤器，以缓解分布间的重叠问题。此外，为从分布本身角度进一步缓解重叠现象，我们提出SelfStepConf方法，通过使用步骤级置信度动态调整推理过程，增强两个分布间的分离度，从而提升投票中置信度的可靠性。在16个模型和5个基准测试上的实验表明，我们的方法显著优于现有最优方法。

English

Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that internal model signals like confidence scores can partly indicate response correctness and exhibit a distributional correlation with accuracy, such distributional information has not been fully utilized to guide answer selection. Motivated by this, we propose DistriVoting, which incorporates distributional priors as another signal alongside confidence during voting. Specifically, our method (1) first decomposes the mixed confidence distribution into positive and negative components using Gaussian Mixture Models, (2) then applies a reject filter based on positive/negative samples from them to mitigate overlap between the two distributions. Besides, to further alleviate the overlap from the perspective of distribution itself, we propose SelfStepConf, which uses step-level confidence to dynamically adjust inference process, increasing the separation between the two distributions to improve the reliability of confidences in voting. Experiments across 16 models and 5 benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches.

相信你的模型：分布引导的置信度校准

Believe Your Model: Distribution-Guided Confidence Calibration

摘要

Support