让大模型学会用恰当语气表达不确定，提升可信度。
Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
- 用模型自身置信度指导微调，生成更准确的不确定表述。
- 在多个问答数据集上验证，单条答案的不确定性表达显著改善。
- 适合需要可信赖输出的医疗、金融等高风险场景使用。
大型语言模型（LLMs）在信息检索和决策任务中应用日益广泛。尽管用途广泛，但它们生成的内容常与现实事实冲突，且其说服性表达容易使错误信息显得自信而可信。这导致用户难以将模型表达的自信程度与其预测准确性对齐，进而产生盲目信任或全盘否定两种极端。本文探索了基于不确定性增强预测的监督微调方法，以训练模型生成校准后的不确定性语言表达。具体而言，我们首先评估预训练模型的校准能力，再通过其自身置信度进行微调，使其生成更可靠的不确定性表述。在多个问答数据集上的实验表明，LLMs在预测评估上具有良好的校准性，基于自身信心的监督微调能有效生成校准良好的不确定性表达，尤其在单条主张回答中表现突出。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. As a result, end-users struggle to consistently align the confidence expressed by LLMs with the accuracy of their predictions, often leading to either blind trust in all outputs or a complete disregard for their reliability. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty. Specifically, we measure the calibration of pre-trained models and then fine-tune language models to generate calibrated linguistic expressions of uncertainty. Through experiments on various question-answering datasets, we demonstrate that LLMs are well-calibrated in assessing their predictions, and supervised finetuning based on the model's own confidence leads to well-calibrated expressions of uncertainty, particularly for single-claim answers.
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