条件语言生成中的幻觉和预测不确定性
On Hallucination and Predictive Uncertainty in Conditional Language Generation
摘要：
尽管不同的自然语言生成任务的性能有所改善，但深度神经模型很容易产生不正确或不存在的幻觉。针对不同的任务提出了不同的假设，并分别进行了检验，但这些任务之间没有系统的解释。在这项研究中，我们绘制幻觉和预测的不确定性之间的联系，在条件语言生成。我们研究了它们在图像描述和数据到文本生成中的关系，并提出了一个简单的扩展束搜索来减少幻觉。我们的分析表明，较高的预测不确定性对应于较高的幻觉机会。认识的不确定性比任意的或完全的不确定性更能说明幻觉。它有助于实现更好的结果，交易性能在标准度量较少的幻觉与所提出的束搜索变体。
英文摘要：
Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent. Different hypotheses are proposed and examined separately for different tasks, but no systematic explanations are available across these tasks. In this study, we draw connections between hallucinations and predictive uncertainty in conditional language generation. We investigate their relationship in both image captioning and data-to-text generation and propose a simple extension to beam search to reduce hallucination. Our analysis shows that higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
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