Datasets › TruthGen

TruthGen

Introduced by Suyash Fulay et al. in On the Relationship between Truth and Political Bias in Language Models9 Sep 2024 archive 2025-07-28

TruthGen is a dataset of generated true and false statements, intended for research on truthfulness in reward models and language models, specifically in contexts where political bias is undesirable. This dataset contains 1,987 statement pairs (3,974 statements in total), with each pair containing one objectively true statement and one false statement. It spans a variety of everyday and scientific facts, excluding politically charged topics to the greatest extent possible. The dataset is particularly useful for evaluating reward models trained for alignment with truth, as well as for research on mitigating political bias while improving model accuracy on truth-related tasks.

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

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

Tasks archive 2025-07-28

License archive 2025-07-28

CC BY 4.0

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • TruthGen

1 variant name, as the archive lists them.

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