The present study introduces the knowledge-augmented generator, which is
specifically designed to produce information that remains grounded in
contextual knowledge, regardless of alterations in the context. Previous
research has predominantly focused on examining hallucinations stemming from
static input, such as in the domains of summarization or machine translation.
However, our investigation delves into the faithfulness of generative question
answering in the presence of dynamic knowledge. Our objective is to explore the
existence of hallucinations arising from parametric memory when contextual
knowledge undergoes changes, while also analyzing the underlying causes for
their occurrence. In order to efficiently address this issue, we propose a
straightforward yet effective measure for detecting such hallucinations.
Intriguingly, our investigation uncovers that all models exhibit a tendency to
generate previous answers as hallucinations. To gain deeper insights into the
underlying causes of this phenomenon, we conduct a series of experiments that
verify the critical role played by context in hallucination, both during
training and testing, from various perspectives.

本研究引入了一种知识增强生成器，旨在生成依托于上下文知识的信息，无论上下文如何变化。我们的研究重点探究在动态知识存在时生成型问答的忠实度，并分析其产生原因。我们提出了一种简单而有效的探测此类幻觉的方法，并发现所有模型都倾向于生成以往的答案作为幻觉。通过一系列实验证明了上下文在幻觉中扮演的关键角色，包括训练和测试阶段，从不同角度获得更深入的洞察。