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Epistemic Diversity and Knowledge Collapse in Large Language Models

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study on epistemic diversity in large language models (LLMs) underscores a critical issue of knowledge collapse, where the outputs of these models become lexically and semantically uniform over time. Conducted with 27 LLMs across 155 topics in 12 countries, the research indicates that despite newer models generating more diverse claims, they remain less diverse than basic web searches. This raises concerns about the accessibility of varied information. The findings also reveal that larger models tend to produce less diverse outputs, while retrieval-augmented generation (RAG) can enhance diversity, albeit with varying success depending on cultural contexts. This research is pivotal as it not only highlights the limitations of current LLMs but also calls for a reevaluation of their design and application to ensure a richer and more diverse knowledge landscape.
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