UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers
Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs. Existing approaches either require manual specification of the feature vocabulary or automate discovery only partially, leaving the gap between dataset-level correlation and model-level exploitation unaddressed. We present U N M ASK, a fully automated pipeline that discovers, causally verifies, and mitigates spurious correlations in text classifiers without additional human annotation. Given unlabeled training examples, U N M ASK...
| Authors | Chidaksh Ravuru, Shashank Srivastava |
|---|---|
| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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