Research4h ago
Scaling Interaction Identification in LLMs
Researchers at BAIR are developing methods to identify interactions within Large Language Models at scale. The work integrates feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. This framework helps model builders isolate specific training examples and internal components driving predictions. It provides a more transparent audit trail for model behavior.