Research1d ago
Scaling Interaction Identification in LLMs
Researchers at BAIR are developing methods to identify interactions at scale within Large Language Models. The team integrates feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. This multi-lens approach targets the transparency gap in complex systems. Practitioners can use these findings to better audit model behaviors and improve safety alignment.