WinoGrande is a large-scale Winograd-style commonsense reasoning dataset introduced to probe pronoun resolution and robust commonsense understanding. Inspired by the original Winograd Schema Challenge, WinoGrande contains ~44k fill-in-the-blank problems with binary options (right/wrong antecedent). Instances were collected via a careful crowdsourcing pipeline and then filtered with an adversarial filtering algorithm (AFLITE) to reduce dataset-specific statistical biases; roughly half the examples were identified as adversarial in the original release. The benchmark is designed to be harder and less exploitable by spurious correlations than earlier WSC variants; reported human performance is very high (~94%) while state-of-the-art models (as of the paper) were substantially lower. The dataset is available from the authors (allenai) and hosted on the Hugging Face datasets hub.
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