大语言模型作为科学领域劳动力增强技术的意外后果
The unintended consequences of large language models as a labor-augmenting technology in science
浏览论文内容
中文总结 AI 辅助
研究大语言模型作为科学领域劳动力增强技术的意外后果，用简单数学模型说明，指出其改变研究精力分配平衡，使研究人员对发表内容选择性改变，还提高时间机会成本，让论文打磨不那么彻底，降温了相关美好期望。
中文摘要 AI 辅助
作为一种劳动力增强技术，大语言模型有潜力加速整个研究流程中的科学活动。但即便大语言模型在特定任务上表现与人类专家相当，其使用也会带来意外后果，因为它改变了引导研究精力在项目间分配的摩擦与诱因的平衡。我们开发了一个简单数学模型来说明这一点。在大语言模型主要作为发现有前景项目工具的领域，研究人员会对发表内容更具选择性；在其促进现有数据发表过程的领域，研究人员选择性会降低。大语言模型虽让科学家工作更快，但提高了研究人员时间的机会成本，促使他们在继续下一步前对论文打磨得没那么彻底。我们的结果让那种认为大语言模型能节省日常任务时间从而让我们有更多时间深入思考和完善项目的希望有所降温。
英文摘要
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
