Research5h ago
Scaling Interaction Identification for LLM Interpretability
Researchers at BAIR are developing methods to identify interactions at scale within large language models. This work bridges feature attribution and mechanistic interpretability to uncover how internal components drive specific predictions. The approach targets the transparency gap in complex systems. Practitioners can use these insights to build more predictable and safer model architectures.