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    <abstract>Benchmarks like MMLU suggest flagship language models approach factuality saturation above 90%. LLMpedia shows this picture is incomplete. We materialize ~1.3M encyclopedia articles entirely from parametric memory across three model families, then audit every claim against Wikipedia and curated web evidence. For gpt-5-mini, the verifiable true rate is 68.4% on Wikipedia-covered subjects — more than 21pp below MMLU — and the gap is driven by 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: https://llmpedia.net</abstract>
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%0 Conference Proceedings
%T LLMpedia: A Transparent Framework to Materialize an LLM’s Encyclopedic Knowledge at Scale
%A Saeed, Muhammed
%A Razniewski, Simon
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F saeed-razniewski-2026-llmpedia
%X Benchmarks like MMLU suggest flagship language models approach factuality saturation above 90%. LLMpedia shows this picture is incomplete. We materialize ~1.3M encyclopedia articles entirely from parametric memory across three model families, then audit every claim against Wikipedia and curated web evidence. For gpt-5-mini, the verifiable true rate is 68.4% on Wikipedia-covered subjects — more than 21pp below MMLU — and the gap is driven by 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: https://llmpedia.net
%U https://aclanthology.org/2026.emnlp-main.2469/
%P 56051-56087

Markdown (Informal)

[LLMpedia: A Transparent Framework to Materialize an LLM’s Encyclopedic Knowledge at Scale](https://aclanthology.org/2026.emnlp-main.2469/) (Saeed & Razniewski, EMNLP 2026)

• LLMpedia: A Transparent Framework to Materialize an LLM’s Encyclopedic Knowledge at Scale (Saeed & Razniewski, EMNLP 2026)

ACL

• Muhammed Saeed and Simon Razniewski. 2026. LLMpedia: A Transparent Framework to Materialize an LLM’s Encyclopedic Knowledge at Scale. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56051–56087, Budapest, Hungary. Association for Computational Linguistics.