Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brasil.
Instituto de Ciências Biológicas (ICB), Universidade Federal do Rio Grande - FURG , Rio Grande, Brasil.
J Gen Physiol. 2024 Nov 4;156(11). doi: 10.1085/jgp.202413654. Epub 2024 Oct 7.
Scholarly publishing has been shaped by the pressure of a liquid economy to become an exercise in branding more than a vehicle for the advancement of science. The current revolution in artificial intelligence (AI) is poised to make matters worse. The new generation of large language models (LLMs) have shown impressive capabilities in text generation and are already being used to write papers, grants, peer review reports, code for analyses, and even perform literature reviews. Although these models can be used in positive ways, the metrics and pressures of academia, along with our dysfunctional publishing system, stimulate their indiscriminate and uncritical use to speed up research outputs. Thus, LLMs are likely to amplify the worst incentives of academia, greatly increasing the volume of scientific literature while diluting its quality. At present, no effective solutions are evident to overcome this grim scenario, and nothing short of a cultural revolution within academia will be needed to realign the practice of science with its traditional ideal of a rigorous search for truth.
学术出版受到液体经济的压力影响,已经变成了一种品牌塑造,而不再是推动科学发展的工具。当前人工智能(AI)的革命可能会使情况变得更糟。新一代的大型语言模型(LLMs)在文本生成方面表现出了令人印象深刻的能力,并且已经被用于撰写论文、资助申请、同行评审报告、分析代码,甚至进行文献综述。尽管这些模型可以被积极地使用,但学术界的指标和压力,以及我们功能失调的出版系统,刺激了它们的不加区分和不加批判的使用,以加速研究成果的产出。因此,大型语言模型可能会放大学术界最糟糕的激励因素,大大增加科学文献的数量,同时降低其质量。目前,没有明显的有效解决方案来克服这种严峻的情况,只有在学术界进行一场文化革命,才能使科学实践重新与追求真理的传统理念保持一致。