  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.
