We argue that this gap stems from a fundamental failure of introspective consistency: AR models agree with what they generate, whereas DLMs often do not.
这是一个令人惊讶的深刻见解，揭示了扩散语言模型(DLMs)与自回归模型(AR)之间性能差距的根本原因。作者提出'内省一致性'概念，指出AR模型天生具有与自身生成内容一致的特性，而DLMs缺乏这种自我验证能力，这为理解DLMs的局限性提供了全新视角。
Mclachlan, S., Lucas, P., Kudakwashe Dube, Hitman, G. A., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. E. (2020). The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach. https://doi.org/10.13140/RG.2.2.27042.66243