Research3h ago
Scaling LLM Interaction Analysis for Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within Large Language Models. The work integrates feature attribution, data attribution, and mechanistic interpretability to map internal decision processes. This approach helps model builders isolate specific input features driving predictions. It provides a technical framework for auditing model transparency.