Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. LLMs are typically evaluated using accuracy, yet this metric does not capture the vulnerability of LLMs to hallucination-inducing factors like prompt and context variability. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? In this paper, we propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability. MONITOR computes the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and contexts.Experiments on a comprehensive range of 12 LLMs demonstrate the effectiveness of MONITOR in evaluating the factual reliability of LLMs while maintaining a low computational overhead. In addition, we release the FKTC (Factual Knowledge Test Corpus) test set, containing 210,158 prompts in total to foster research along this line (https://github.com/Vicky-Wil/MONITOR).

本文提出了一种名为MONITOR的新度量方法，用于直接衡量大型语言模型的事实可靠性，通过计算有效输出与同一模型使用不同类型提示和上下文进行探索所产生的对应输出之间的概率分布距离来评估模型的一致性。实验证明MONITOR对于评估大型语言模型的事实可靠性具有良好的效果，并且计算开销较低。此外，作者还发布了包含210,158个提示的FKTC测试集，以促进相关研究的开展。

评估大型语言模型知识的可靠性