We explore the relationship between factuality and Natural Language Inference
(NLI) by introducing FactRel -- a novel annotation scheme that models
\textit{factual} rather than \textit{textual} entailment, and use it to
annotate a dataset of naturally occurring sentences from news articles. Our
analysis shows that 84\% of factually supporting pairs and 63\% of factually
undermining pairs do not amount to NLI entailment or contradiction,
respectively, suggesting that factual relationships are more apt for analyzing
media discourse. We experiment with models for pairwise classification on the
new dataset, and find that in some cases, generating synthetic data with GPT-4
on the basis of the annotated dataset can improve performance. Surprisingly,
few-shot learning with GPT-4 yields strong results on par with medium LMs
(DeBERTa) trained on the labelled dataset. We hypothesize that these results
indicate the fundamental dependence of this task on both world knowledge and
advanced reasoning abilities.

探索事实性与自然语言推理之间的关系，并引入 FactRel 注释方案来模拟事实性推理，分析表明，事实性支持对和事实性削弱对的大部分情况不构成自然语言推理的蕴含或矛盾关系，这表明事实关系更适合分析媒体话语；在新数据集上进行了对比分类模型的实验，并发现在某些情况下，基于注释数据集使用 GPT-4 生成合成数据可以提高性能，而使用 GPT-4 进行少样本学习的结果与在标注数据集上训练的中型语言模型（DeBERTa）相当强大，这些结果表明此任务在世界知识和高级推理能力上的基本依赖关系。