Large Language Models (LLMs), including ChatGPT and LLaMA, are susceptible to
generating hallucinated answers in a confident tone. While efforts to elicit
and calibrate confidence scores have proven useful, recent findings show that
controlling uncertainty must go beyond calibration: predicted scores may
deviate significantly from the actual posterior probabilities due to the impact
of grouping loss. In this work, we construct a new evaluation dataset derived
from a knowledge base to assess confidence scores given to answers of Mistral
and LLaMA. Experiments show that they tend to be overconfident. Further, we
show that they are more overconfident on some answers than others, \emph{eg}
depending on the nationality of the person in the query. In
uncertainty-quantification theory, this is grouping loss. To address this, we
propose a solution to reconfidence LLMs, canceling not only calibration but
also grouping loss. The LLMs, after the reconfidencing process, indicate
improved confidence alignment with the accuracy of their responses.

大型语言模型 (LLMs)，包括 ChatGPT 和 LLaMA，在以自信的口吻生成虚构答案方面容易出错。本文通过构建来自知识库的新评估数据集，评估了 Mistral 和 LLaMA 给出的答案的置信度分数，并展示它们倾向于过于自信。我们还发现它们在一些答案上比在其他答案上更为自信，例如取决于查询中的人的国籍。为了解决这个问题，我们提出了一种重新确定置信度的方法，取消了校准与分组损失。在重新确定置信度的过程中，语言模型表明其响应的准确性与其置信度的对齐有所改善。