Abstract
Large Language Models (LLMs) often generate outputs that lack grounding in real-world facts, a phenomenon known as hallucinations. Prior research has associated hallucinations with model uncertainty, leveraging this relationship for hallucination detection and mitigation. In this paper, we challenge the underlying assumption that all hallucinations are associated with uncertainty. Using knowledge detection and uncertainty measurement methods, we demonstrate that models can hallucinate with high certainty even when they have the correct knowledge. We further show that high-certainty hallucinations are consistent across models and datasets, distinctive enough to be singled out, and challenge existing mitigation methods. Our findings reveal an overlooked aspect of hallucinations, emphasizing the need to understand their origins and improve mitigation strategies to enhance LLM safety. The code is available at this https URL .
Abstract (translated)
大型语言模型（LLMs）经常生成缺乏现实世界事实依据的输出，这种现象被称为幻觉。先前的研究将幻觉与模型不确定性联系起来，并利用这一关系进行幻觉检测和缓解。在本文中，我们挑战了所有幻觉都与不确定性相关的基本假设。通过知识检测和不确定度测量方法，我们证明即使拥有正确的知识，模型也可以以高度确信的方式产生幻觉。此外，我们还表明，高确定性的幻觉在不同的模型和数据集中是一致且独特的，并对现有的缓解方法提出了挑战。我们的发现揭示了幻觉的一个被忽视的方面，强调了理解其起源并改进缓解策略以增强LLM安全性的必要性。代码可在[此处](https://example.com)获取。
URL
https://arxiv.org/abs/2502.12964
