---
title: 'TruthfulQA: Measuring How Models Mimic Human Falsehoods'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2109-07958-truthfulqa.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Stephanie Lin, Jacob Hilton, Owain Evans, TruthfulQA: Measuring How Models Mimic Human Falsehoods (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: evals'
tags: arxiv, evals, factuality, honesty
arxiv_id: 2109.07958
alias: 2109.07958-truthfulqa
type: pointer
format: pointer
sources:
  - id: S1
    title: 'TruthfulQA: Measuring How Models Mimic Human Falsehoods'
    publisher: arXiv
    authors: 'Stephanie Lin, Jacob Hilton, Owain Evans'
    url: https://arxiv.org/abs/2109.07958
    version_read: 'v2 (8 May 2022)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# TruthfulQA: Measuring How Models Mimic Human Falsehoods

## Summary
[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://arxiv.org/abs/2109.07958) (2021) measures whether a model repeats popular falsehoods rather than giving a true answer. It became a core honesty and factuality evaluation, because a fluent answer can still be the common myth.

What it introduced: a question set designed so that the most likely imitative answer is often false, plus an evaluation protocol for whether models stay truthful on those questions.

What it led to: a standard reported number in later model cards and technical reports, and a line of work on factuality and hallucination. Live notes on evaluation practice include [ao-evals-agent-benchmarks-and-judges](https://iinventedlight.com/library/notes/ao-evals-agent-benchmarks-and-judges/).

Use it when a fluent answer is not enough and you need a test of whether the model echoes a common myth. Where to read it: https://arxiv.org/abs/2109.07958 (v2, last revised 8 May 2022). ACL 2022 (main conference).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).