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Scientific Production in the Era of Large Language Models: Early Evidence from Large-scale Preprint Data

May 26 @ 2:00 pm - 3:00 pm

May 26 @ 2:00 pm 3:00 pm

Details

Tuesday 26 May at 9:00 (EDT) / 14:00 (BST) / 15:00 (CEST)

Speaker: Yian Yin, Cornell University, USA

About the seminar

The rapid adoption of AI across disciplines is reshaping the landscape of scientific production. While both enthusiasm and concern about generative AI in research are rising, systematic empirical evidence on the impact of large language models (LLMs) remains limited. In this talk, I draw on several large-scale analyses to examine how LLM use affects the productivity of individual scientists, reshapes attention to prior work, introduces hallucinated content into the scientific record, and creates new challenges for peer review. Taken together, these findings provide macro-level evidence on the impact of generative AI on science, highlighting the need for institutions, journals, funding agencies, and the broader public to rethink how scientific work should be evaluated in this new era.

About the speaker

Yian Yin is Assistant Professor of Information Science at Cornell University. His research interests lie at the intersection of network science and computational social science, with a particular focus on the science of science. He applies and develops novel computational tools to understand how individual, social, and environmental processes independently and jointly promote (or inhibit) scientific progress and innovation achievements.

His research has been published in journals including Science, Nature and Nature Human Behaviour, featured in media outlets such as Forbes, Scientific American, Washington Post, and Harvard Business Review, and supported by research grants from National Science Foundation, National Aeronautics and Space Administration, Schmidt Sciences, and UK Economic and Social Research Council.

Register in advance for this seminar:

https://us02web.zoom.us/meeting/register/JF0DKNl3TFehf54YHgLmuA

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