ConfTuner: 训练大型语言模型以口头表达其置信度
ConfTuner: Training Large Language Models to Express Their Confidence Verbally
AI总结：
ConfTuner通过引入标记化布里尔分数损失函数，有效提升大型语言模型的置信度校准能力，适用于多种推理任务并泛化至黑盒模型。
摘要：
大型语言模型（LLMs）越来越多地应用于高风险领域，如科学、法律和医疗，其中准确表达不确定性对于可靠性和信任至关重要。然而，当前的LLMs常常被观察到在高置信度下生成错误答案，这种现象被称为
英文摘要：
Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as science, law, and healthcare, where accurate expressions of uncertainty are essential for reliability and trust. However, current LLMs are often observed to generate incorrect answers with high confidence, a phenomenon known as "overconfidence". Recent efforts have focused on calibrating LLMs' verbalized confidence: i.e., their expressions of confidence in text form, such as "I am 80% confident that...". Existing approaches either rely on prompt engineering or fine-tuning with heuristically generated uncertainty estimates, both of which have limited effectiveness and generalizability. Motivated by the notion of proper scoring rules for calibration in classical machine learning models, we introduce ConfTuner, a simple and efficient fine-tuning method that introduces minimal overhead and does not require ground-truth confidence scores or proxy confidence estimates. ConfTuner relies on a new loss function, tokenized Brier score, which we theoretically prove to be a proper scoring rule, intuitively meaning that it "correctly incentivizes the model to report its true probability of being correct". ConfTuner improves calibration across diverse reasoning tasks and generalizes to black-box models such as GPT-4o. Our results further show that better-calibrated confidence enables downstream gains in self-correction and model cascade, advancing the development of trustworthy LLM systems. The code is available at https://github.com/liushiliushi/ConfTuner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
