Research4h ago
Scaling LLM Interaction Analysis for Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within Large Language Models. The approach combines feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. This framework helps developers isolate specific input features and training examples. It provides a more transparent audit trail for model behavior.