Research1d ago
Scaling Interaction Identification for LLM Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within large language models. This work bridges feature attribution and mechanistic interpretability to reveal how internal components drive specific predictions. The approach helps developers pinpoint why models fail. It offers a more precise toolkit for auditing model behavior than standard attribution methods.