SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself.
大多数人认为不同架构的模型会有不同的失败模式和弱点,但作者发现无论架构和参数规模如何,SOTA模型在相同困难样本上表现出高度一致的失败模式,这表明性能瓶颈源于训练数据的共同缺陷,而非架构差异,这一发现挑战了模型多样化的传统观点。