Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these system...
The development of SPEX and ProxySPEX represents a significant advancement in AI interpretability, addressing a critical gap in understanding how complex models synthesize information. The strongest version of this narrative highlights the frameworks' ability to efficiently uncover high-order interactions—something marginal attribution methods fail to capture. For example, in the trolley problem case, SPEX revealed that the model's behavior was driven not by individual words but by their synergi...