Evals & Safety
A benchmark of 817 questions across 38 categories, including health, law, finance, and politics, written so that a common human misconception produces a confident wrong answer.
A benchmark of 817 questions across 38 categories, including health, law, finance, and politics, written so that a common human misconception produces a confident wrong answer. It tests whether a model repeats false beliefs it absorbed from training text. In the original 2021 study, the best model was truthful on 58% of questions against 94% for humans, and larger models were generally less truthful.
Primary source: arxiv.org
Example
In practice, developers reach for TruthfulQA when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Evals & Safety.
FAQ
A benchmark of 817 questions across 38 categories, including health, law, finance, and politics, written so that a common human misconception produces a confident wrong answer.
TruthfulQA sits in the Evals & Safety part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Evals & Safety topics including TruthfulQA. Check the blog and YouTube channel for hands-on walkthroughs.
Related
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Benchmark contamination happens when a model's training data contains the questions or answers from an evaluation benchmark.

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