In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e.g., Wikipedia) to low-resourced domains that lack human annotations. To this end, we first construct a benchmark dataset collection which contains 11 FV datasets representing 6 domains. We conduct an empirical analysis of generalization across these FV datasets, finding that current models generalize poorly. Our analysis reveals that several factors affect generalization, including dataset size, length of evidence, and the type of claims. Finally, we show that two directions of work improve generalization: 1) incorporating domain knowledge via pretraining on specialized domains, and 2) automatically generating training data via claim generation.

我们研究了事实验证（FV）的零样本和小样本泛化，目标是将在资源丰富领域（如维基百科）上训练的FV模型推广到缺乏人类注释的低资源领域。通过构建包含11个FV数据集的基准数据集集合，我们对这些FV数据集之间的泛化进行了实证分析，发现现有模型的泛化能力较差。我们的分析揭示了几个影响泛化的因素，包括数据集大小、证据长度和索赔类型。最后，我们展示了两个改进泛化能力的方法：1）通过预训练在特定领域中引入领域知识，2）通过索赔生成自动生成训练数据。

研究事实验证中的零样本和少样本泛化能力