Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequences. Despite much recent work in confidence estimation, current evaluation frameworks rely on correctness functions -- various heuristics that are often noisy, expensive, and possibly introduce systematic biases. These methodological weaknesses tend to distort evaluation metrics and thus the comparative ranking of confidence measures. We introduce MCQA-Eval, an evaluation framework for assessing confidence measures in Natural Language Generation (NLG) that eliminates dependence on an explicit correctness function by leveraging gold-standard correctness labels from multiple-choice datasets. MCQA-Eval enables systematic comparison of both internal state-based white-box (e.g. logit-based) and consistency-based black-box confidence measures, providing a unified evaluation methodology across different approaches. Through extensive experiments on multiple LLMs and widely used QA datasets, we report that MCQA-Eval provides efficient and more reliable assessments of confidence estimation methods than existing approaches.

本文解决了当前自然语言生成中信心评估依赖于不准确而昂贵的正确性函数的问题。通过引入MCQA-Eval框架，利用多选数据集中的金标准正确性标签，提出了一种新的评估方法，该方法能系统比较不同类型的信心评估方法。实验结果表明，MCQA-Eval在效率和可靠性上均优于现有方法。

MCQA评估：基于金标准准确性标签的自然语言生成中高效的信心评估