Datasets › CODAH

CODAH (COmmonsense Dataset Adversarially-authored by Humans)

Introduced by Michael Chen et al. in CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense archive 2025-07-28

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. It contains 2801 questions in total, and uses 5-fold cross validation for evaluation.

Source: CODAH Dataset Image Source: https://www.aclweb.org/anthology/W19-2008.pdf

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 29. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Generative Data Augmentation for Commonsense Reasoning 1 1 24 Apr 2020 ran 2 of 4 samples (2 unverified; 4 pointer-only for licence)
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense 2 2 8 Apr 2019 not harvested

Dataset loaders archive 2025-07-28

3 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Unknown

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • CODAH

1 variant name, as the archive lists them.

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