3 papers
cs.DL2026
A robust association between LLM use and scientific productivity: Assessing stopping-time selection
Keigo Kusumegi, Xinyu Yang, Paul Ginsparg +3
Renault, Bergeaud, and Bosquet (hereafter RBB) argue that dating LLM adoption as the first month in which an author's abstract is flagged induces a stopping-time selection that can…
cs.DL2026
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
Zhenyue Zhao, Yihe Wang, Toby Stuart +3
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 hallucinat…
cs.DL2026
Scientific production in the era of Large Language Models
Keigo Kusumegi, Xinyu Yang, Paul Ginsparg +3
Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and…