Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models, or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task called indirect object identification (IOI). Our explanation encompasses 26 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches relying on causal interventions. To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model. We evaluate the reliability of our explanation using three quantitative criteria--faithfulness, completeness and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. Our work provides evidence that a mechanistic understanding of large ML models is feasible, opening opportunities to scale our understanding to both larger models and more complex tasks.

本文通过探究 GPT-2 small 在间接宾语识别等自然语言任务中的表现来解释仿真学习模型的行为，其中提及了26种注意力头，本研究是目前规模最大的一次试图从机器学习模型的内部组件入手逆向工程自然行为。我们以及利用了因果干预等解释性方法对我们的解释进行了可靠性评估，工作证明了对大型机器学习模型的机械理解是可行的。

自然语言生成模型中的间接对象识别电路实现及其解释性