What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models

Abstract

Large language models (LLMs) are increasingly used as primary knowledge sources, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - has never been measured. Low epistemic diversity would pose a risk of knowledge collapse as homogeneous LLMs mediate a shrinking in the range of accessible information over time. The dominant paradigm is that overall LLM diversity is low, but this is always with respect to a single point in time, with no reference baseline or consideration for variation across countries. We address this gap in knowledge by performing the first systematic study of epistemic diversity in LLMs across time and cultural context, testing 27 LLMs on 155 topics covering 12 countries, resulting in 1.7M responses and 70M individual claims. We find that epistemic diversity has increased substantially over the past three years, a positive counter to recent diversity pessimism. However, despite progress, we find that every system is less diverse than a search baseline. This gap is not uniform: RAG can improve diversity, while large models are counterintuitively less diverse than smaller ones. Moreover, LLM parametric knowledge systematically reflects English over local-language knowledge for country specific topics. Together, these results demonstrate that while progress on epistemic diversity is tangible, it is insufficient and unevenly distributed.

Accepted to EMNLP 2026; 18 pages, 8 figures, 4 tables; v2: Fixed the modeling for table 3, random effect is the model version; v3: Fixed minor formatting issues in tables 2 and 3; v4: Fixed some typos and model description; v5: Updated metadata; v6: Improved search baseline, writing revisions, added comparisons to semantic similarity only approaches; v7 changelog: EMNLP camera ready