Research13h 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 internal decision-making processes. This approach moves beyond isolated analysis. It provides model builders with a more transparent framework to audit LLM behaviors and improve safety.