AI & Machine Learning

Where are a benchmark's gold labels least decisive? A per-genre annotator-agreement census of MultiNLI

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A recent analysis of the MultiNLI benchmark revealed that the "gold label" decisiveness varies significantly by text genre, contrary to expectations that informal, spoken text would be noisier. Formal genres like government and slate writing produced the least agreement among annotators, while letters and travel genres showed higher consensus. This suggests that textual formality does not directly correlate with inferential clarity or annotator agreement, challenging the assumption that cleaner text always leads to more reliable labels.

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