LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale

Abstract: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 overlap in just 7.3\% of subject choices. In a retrieval-trap benchmark inspired by prior analysis of Grokipedia, LLMpedia is more factual at roughly half the textual similarity to Wikipedia. Every prompt, article, and verdict is released. Data, code, interface: this https URL.

Computation and Language (cs.CL); Databases (cs.DB)

arXiv:2603.24080 [cs.CL]

(or arXiv:2603.24080v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.24080 Focus to learn more arXiv-issued DOI via DataCite

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