This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance the public's understanding of science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work first assessed linguistic simplicity differences across such summaries and public perceptions in follow-up experiments. Specifically, Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used a large language model, GPT-4, to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable perceptions of scientists (they were perceived as more credible and trustworthy, but less intelligent) than more complexly-written human PNAS summaries. Crucially, Study 3 experimentally demonstrated that participants comprehended scientific writing better after reading simple GPT summaries compared to complex PNAS summaries. In their own words, participants also summarized scientific papers in a more detailed and concrete manner after reading GPT summaries compared to PNAS summaries of the same article. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society.

翻译：本研究评估了利用生成式人工智能简化科学传播并提升公众科学理解的有效性。通过比较《美国国家科学院院刊》（PNAS）期刊文章的人工撰写公众摘要与AI生成摘要，本文首先评估了此类摘要的语言简洁性差异，并在后续实验中考察了公众认知。具体而言，研究1a分析了PNAS摘要（科学摘要）与意义陈述（公众摘要）的简洁性特征，发现公众摘要确实在语言上更简单，但效应量差异较小。研究1b使用大型语言模型GPT-4基于论文摘要生成意义陈述，未经微调即将平均效应量提升至两倍以上。研究2通过实验证明：与语言复杂的人类撰写的PNAS摘要相比，GPT生成的简洁摘要能使科学家获得更积极的认知（被感知为更可信可靠，但智力评价较低）。关键的是，研究3通过实验证实：参与者在阅读简洁的GPT摘要后，比阅读复杂的PNAS摘要能更好地理解科学文本。参与者用自身语言复述论文时，阅读GPT摘要后的总结比阅读同一文章的PNAS摘要更具体详尽。人工智能通过简洁语言启发式，具备连接科学界与公众的潜力，我们主张将其整合到科学传播体系中，以构建更具科学素养的社会。