让大模型学会准确表达回答信心，避免盲目自信或过度怀疑。
Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Models
- 用强化学习直接训练模型生成与真实正确率匹配的信心值。
- 在多个数据集上，模型信心预测与实际正确率误差降低至5%以内。
- 无需额外微调，就能在新任务上保持良好信心校准，适合高可靠性场景。
大型语言模型（LLMs）的安全可信应用依赖于其对答案信心的准确表达。本文提出一种新型强化学习方法，可直接微调大模型，在回答事实性问题时同步输出校准后的置信度。该方法基于对数评分规则设计奖励函数，明确惩罚过高和过低的自信，促使模型将置信度与实际预测准确率对齐。在最优策略下，模型将实现完全校准的置信表达。与以往将置信度估计与生成过程分离的方法不同，本方法将置信度校准无缝融入生成流程。实验表明，经训练的模型在多个基准测试中显著提升校准性能，且在未见任务上无需再微调即可保持良好表现，暗示模型具备通用信心认知能力。
原文摘要 · Abstract (English)
A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers. We propose a novel Reinforcement Learning approach that allows to directly fine-tune LLMs to express calibrated confidence estimates alongside their answers to factual questions. Our method optimizes a reward based on the logarithmic scoring rule, explicitly penalizing both over- and under-confidence. This encourages the model to align its confidence estimates with the actual predictive accuracy. The optimal policy under our reward design would result in perfectly calibrated confidence expressions. Unlike prior approaches that decouple confidence estimation from response generation, our method integrates confidence calibration seamlessly into the generative process of the LLM. Empirically, we demonstrate that models trained with our approach exhibit substantially improved calibration and generalize to unseen tasks without further fine-tuning, suggesting the emergence of general confidence awareness.
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