Natural Language Inference (NLI) is foundational for evaluating language
understanding in AI. However, progress has plateaued, with models failing on
ambiguous examples and exhibiting poor generalization. We argue that this stems
from disregarding the subjective nature of meaning, which is intrinsically tied
to an individual's \textit{weltanschauung} (which roughly translates to
worldview). Existing NLP datasets often obscure this by aggregating labels or
filtering out disagreement. We propose a perspectivist approach: building
datasets that capture annotator demographics, values, and justifications for
their labels. Such datasets would explicitly model diverse worldviews. Our
initial experiments with a subset of the SBIC dataset demonstrate that even
limited annotator metadata can improve model performance.

自然语言推理（NLI）是评估人工智能语言理解的基础。我们认为，现有模型在模糊的例子上失败并且表现出差劲的泛化能力，这是由于忽视了意义的主观性，它与个体的世界观密切相关。我们提出了一种观点主义的方法：构建捕捉注解者人口统计、价值观和标签理由的数据集。这样的数据集将明确地建模多样化的世界观。我们对 SBIC 数据集的一个子集进行的初步实验表明，即使是有限的注解者元数据也能提高模型的性能。