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 (this https URL).

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。