LLM hallucinations in the wild: Large-scale evidence from non-existent citations

TL;DR

This study provides large-scale evidence that LLMs increasingly generate false scientific citations, which threaten the reliability and equity of scientific knowledge as their adoption grows.

Contribution

It offers the first large-scale, empirical analysis of LLM hallucinations in scientific citations across millions of papers, revealing their prevalence and societal implications.

Findings

Sharp rise in non-existent references post-LLM adoption

Hallucinated citations disproportionately affect certain fields and demographics

Current moderation processes only partially mitigate these errors

Abstract

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find a sharp rise in non-existent references following widespread LLM adoption, with a conservative estimate of 146,932 hallucinated citations in 2025 alone. These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake, in manuscripts with linguistic signatures of AI-assisted writing, and among small and early-career author teams. At the same time, hallucinated references disproportionately assign credit to already prominent and male scholars, suggesting that LLM-generated errors may reinforce existing inequities in scientific recognition. Preprint moderation and journal publication processes capture only a fraction of these errors, suggesting that the spread of hallucinated content has outpaced existing safeguards. Together, these findings demonstrate that LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature.