Datasets › GIGO revisited: ML publications' approaches to training data

GIGO revisited: ML publications' approaches to training data

Introduced by R. Stuart Geiger et al. in "Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?5 Jul 2021 archive 2025-07-28

A random sample of 200 machine learning publications, systematically analyzed by a team of labelers, who asked up to 15 questions about how the publication discusses its training data. More documentation in data/README.md.

Homepage JSON

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

No task tagged in the archive.

License archive 2025-07-28

MIT License

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

English

Variants archive 2025-07-28

GIGO revisited: ML publications' approaches to training data

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections