生成器是否关注其上下文？上下文迁移下生成模型忠实性分析
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
AI总结：
本文研究生成式问答在上下文动态变化时的忠实性问题，提出一种简单有效的幻觉检测方法，并通过实验验证上下文在训练与测试阶段对幻觉产生的关键作用。
摘要：
本研究引入了知识增强生成器，该生成器专门设计用于生成始终以上下文知识为依据的信息，无论上下文如何变化。以往的研究主要关注静态输入产生的幻觉，例如在摘要生成或机器翻译领域。然而，我们的研究深入探讨了在动态知识存在的情况下生成式问答的忠实性。我们的目标是探索当上下文知识发生变化时，由参数化记忆产生的幻觉是否存在，同时分析其出现的根本原因。为了高效地解决这一问题，我们提出了一种简单而有效的检测此类幻觉的方法。有趣的是，我们的研究发现，所有模型都倾向于将先前的答案生成为幻觉。为了更深入地理解这一现象的根本原因，我们进行了一系列实验，从多个角度验证了上下文在训练和测试阶段对幻觉所起的关键作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
