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Which, whose, and how much knowledge do LLMs represent? I'm excited to share our preprint answering these questions: "Epistemic Diversity and Knowledge Collapse in Large Language Models" 📄Paper: arxiv.org/pdf/2510.04226 💻Code: github.com/dwright37/ll... 1/10

🤔 A lot of people are using LLMs. However, their outputs are not very diverse. What does this mean for the future of knowledge? Many speculate that overreliance on LLMs will lead to "knowledge collapse", where the diversity of human knowledge is narrowed by a reliance on homogenous LLMs. 2/10

👍 To assess this risk, we set out to measure to what extent LLMs are homogenous in terms of the *real-world claims* they generate. We perform a large study across 27 LLMs, 2 generation settings, with different model versions and sizes. In a nutshell, our findings are: 3/10

📈 Knowledge in LLMs across 3 of 4 model families has *expanded* since 2023 ✅ ; however, their absolute diversity is quite low compared to a very modest traditional search baseline 👎 4/10

🌍 There are gaps in country specific knowledge. When matching claims to English and local language Wikipedia, no local language is statistically significantly more represented than English, and English language knowledge is statistically significantly more represented for 5 of 8 countries 6/10

🪛 Approach: we propose a new methodology which includes sampling plain text LLM outputs with 200 prompt variations from real chats across 155 topics, decomposing into individual claims, and clustering those claims based on entailment. 7/10

📏 To measure diversity we use a statistically grounded measure commonly used to measure species diversity in ecology, in order to fairly compare the relative diversity of models in different settings. 8/10