Title: Can Interpretation Predict Behavior on Unseen Data?
Primary area: interpretability and explainable AI
Keywords: interpretabilty, generalization, out-of-distribution

Abstract: Interpretability research often aims to predict how a model will respond to targeted interventions on specific mechanisms. However, it rarely predicts how a model will respond to unseen input data. This paper explores the promises and challenges of interpretability as a tool for predicting out-of-distribution (OOD) model behavior.  Specifically, we investigate the correspondence between attention patterns and OOD generalization in hundreds of Transformer models independently trained on a synthetic classification task. These models exhibit several distinct systematic generalization rules OOD, forming a diverse population for correlational analysis. In this setting, we find that simple observational tools from interpretability can predict OOD performance. In particular,  when in-distribution attention exhibits hierarchical patterns, the model is likely to generalize hierarchically on OOD data---even when the rule's implementation does not rely on these hierarchical patterns, according to ablation tests. Our findings offer a proof-of-concept to motivate further interpretability work on predicting unseen model behavior.

TL;DR: Proof-of-concept that interpretability can be used to predict future model behavior on unseen inputs