Research3d ago
Scaling Interaction Identification for LLM Interpretability
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 decision-making processes. This framework helps builders isolate specific input drivers and internal components. It provides a technical blueprint for auditing model behavior without relying on manual inspection.