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TruthfulQA

Reasoning / Safety

TruthfulQA measures whether a model repeats falsehoods that people commonly believe. Its 817 questions span 38 categories including health, law, finance and politics, and each one is written to invite a plausible wrong answer.

truthfulqa

Full text200 tasksProblems imported

Official source

Full text: eligible public problems include text, images and discussion.

At a glance

What a problem looks like

One question, written to invite a plausible falsehood. Its category, its adversarial type and the source documenting the misconception all narrow the answer, so none of them is published.

Read an actual problem

How it is scored

The source ships a best answer plus lists of correct and incorrect answers; this catalogue keeps all of them in restricted storage and never renders them.

Metric: truthfulness and informativeness, judged per answer; the multiple-choice configuration reports MC1/MC2 accuracy

Why it is hard

Every question has an answer that sounds right because people repeat it. Scale alone tends to make this worse, because the falsehood is well represented in the training text.

Cite

@article{lin2021truthfulqa,
  title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
  author={Stephanie Lin and Jacob Hilton and Owain Evans},
  journal={arXiv preprint arXiv:2109.07958},
  year={2021}
}

Discussion

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Tasks

Whole public benchmark.

Public problems
200
Formats
text (200)
Problems with images
0
Answer availability
Answer published by the source (200)
Configs
generation (200)
Splits
validation (200)
Awaiting a first discussion
200
Problems with a discussion
0
Public contributions
0

Provenance and terms

Pinned source revisions, newest first:

  • 741b8276f2d1982aa3d5b832d3ee81ed3b896490 · acquired 2026-09-12 · release 28ecf922883b