Study Reveals Large Language Models Fall Short of Factuality Claims, With Only 68% Accuracy on Verifiable Topics
Researchers generated approximately 1.3 million encyclopedia articles from large language models and audited every claim against Wikipedia and web evidence, finding GPT-5-mini's verifiable accuracy at just 68.4% on Wikipedia-covered subjects. This is more than 21 percentage points below what MMLU benchmark scores would suggest, with the gap driven primarily by unverifiable claims (30.5%) rather than outright false ones (1.2%). The findings challenge the widespread assumption that high benchmark scores reflect genuine factual reliability in open-ended knowledge generation.
A new preprint introduces LLMpedia, a framework that materializes large-scale encyclopedic content entirely from the parametric memory of language models, then systematically audits each claim for factual accuracy. Across three model families and roughly 1.3 million generated articles, the study finds that GPT-5-mini achieves a verifiable true rate of 68.4% on subjects covered by Wikipedia — a result more than 21 percentage points below what MMLU benchmark performance would imply. The dominant driver of this gap is not factual error but unverifiability: 30.5% of claims could not be confirmed or refuted, while only 1.2% were directly contradicted. When auditing frontier articles against curated web evidence beyond Wikipedia, accuracy dropped further to 57.6%, and Wikipedia itself covers only 56.7% of the subjects models chose to write about. Notably, the three model families overlapped on just 7.3% of subject choices, suggesting substantial divergence in what knowledge each model has internalized. The researchers also introduced a retrieval-trap benchmark and found LLMpedia articles were more factual than a prior comparable system while showing roughly half the textual similarity to Wikipedia, indicating less verbatim copying. All prompts, articles, and audit verdicts have been publicly released.
What's missing
The paper is a preprint and has not yet undergone formal peer review.
What different sources said
- arXiv cs.CLCenter
LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale
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