Research5h ago
Scaling Interaction Identification for LLM Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within large language models. This approach combines feature attribution and mechanistic interpretability to map how internal components drive specific predictions. The framework helps developers pinpoint the exact training examples influencing model behavior. It provides a concrete path toward auditing complex neural networks.