用大语言模型自动构建强简单基线以检验深度学习贡献
原标题：Automatic Construction of Strong Simple Baselines
AI 摘要
该研究从 Nature、Science 和 Cell 系列期刊的同行评审论文中收集了 36 个监督分类与回归任务，这些论文均提出了新的深度学习方法。研究者使用大语言模型为每个任务自动构建简单基线，发现这些基线能恢复平凡预测器与深度学习模型之间差距的中位数 95.5%（均值 85.7%），且在 12 项研究中中位数基线达到或超过深度学习模型的表现。作者认为，自动基线构建可作为人工同行评审的可扩展补充，用于核查物理与生命科学中深度学习贡献的真实性。
正文节选
Automatic Construction of Strong Simple Baselines Abstract To rigorously establish the value of a novel deep learning method, it must be compared against appropriate baselines. As deep learning has become increasingly common in the physical and life sciences, there have been numerous cases in which apparent progress has turned out to be an artifact of omitted or missing baselines. In this work, we investigate whether this problem can be mitigated by using large language models to automate the co