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Found 5 papers, 4 papers with code
What's In My Big Data?
We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them.
Elaboration-Generating Commonsense Question Answering at Scale
In question answering requiring common sense, language models (e. g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
