Research2d ago
Scaling Interaction Identification for LLM Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within large language models. The work combines feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. This approach moves beyond isolating single features to understand complex component dependencies. Practitioners gain a more transparent framework for auditing model behavior and improving safety.