Research17h ago
Scaling Interaction Identification in LLMs
Researchers at BAIR are developing methods to identify interactions at scale within large language models. They combine feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. This approach moves beyond isolated analysis to provide a comprehensive view of model behavior. Practitioners can now better audit how specific training data influences final predictions.