LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale
Benchmarks like MMLU suggest flagship language models approach factuality saturation above 90\%. \emph{LLMpedia} shows this picture is incomplete. We materialize ${\sim}$1.3M encyclopedia articles entirely from parametric memory across three model families, then audit every claim against Wikipedia and curated web evidence. For \texttt{gpt-5-mini}, the verifiable true rate is 68.4\% on Wikipedia-covered subjects - more than 21\,pp below MMLU - and the gap is driven by \emph{unverifiability} (30.5\%), not refutation (1.2\%). Beyond Wikipedia, frontier articles audited against curated web evidence reach 57.6\%; Wikipedia covers only 56.7\% of model-surfaced subjects, and three model families...
| Authors | Muhammed Saeed, Simon Razniewski |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 10 June 2026 |
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