Adoption of LLMs will cause scientists to 'do more, less well ' rather than the same amount, better', write the authors. But they say that such artificial-intelligence tools do not bear all of the blame for that outcome. The results reflect a flawed incentive system in science that prioritizes quantity over quality, says study co-author Carl Bergstrom, a biologist at the University of Washington in Seattle. Scientists are incentivized to churn out papers, he says, and LLMs help them to reach this ever-growing quota. 'LLMs are rarely the problem themselves,' says Bergstrom. 'LLMs hold up a mirror to problems that we already have.' The study was posted on the arXiv preprint repository on 19 July and has not yet been peer reviewed. To predict how LLMs will change scientific productivity, the authors broke the research process into discrete phases. First comes a discovery phase when scientists brainstorm hypotheses and conduct initial experiments to determine the value of a project....
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