Research17h ago
Scaling Interaction Identification for LLM Interpretability
Researchers at BAIR are developing methods to identify interactions within large language models at scale. This approach combines feature attribution, data attribution, and mechanistic interpretability to map internal decision-making. By isolating specific input features and training examples, the team aims to make model behavior transparent. This provides a technical framework for auditing complex neural networks.