As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a challenge. We propose APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or adjusting the given answer based on the confidence. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.

利用APRICOT方法，通过模型的文本输入和输出，设置置信度目标并训练附加模型，从而有效地进行大语言模型的置信度校准。该方法不仅概念简单，而且不需要访问目标模型的其他信息，不会干扰语言生成，并且在封闭型问答中以检测错误答案方面具有竞争性的校准误差表现。

仅使用生成进行大型语言模型的校准