Exploring the role of large language models in the scientific method: from hypothesis to discovery
Yanbo Zhang, Sumeer Ahmad Khan, A. K. M. Firoj Mahmud, Huck Yang, Alexander Lavin, Michael Levin, Jeremy G. Frey, Jared Dunnmon, James H. Evans, Alan Bundy, Sašo Džeroski, Jesper Tegnér, Héctor Zenil
Abstract
Abstract We review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics.
Topics & Concepts
Scientific discoveryComputer sciencePsychologyData scienceCognitive scienceEpistemologyLinguisticsPhilosophyTopic ModelingMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education