Research5d ago
Scale‑Sized Interaction Mapping for LLMs
The study dissects 3,000 internal components of LLMs to map interactions at scale. It combines feature attribution, data attribution, and mechanistic interpretability to isolate drivers of predictions. The approach clarifies which training examples and internal modules shape outputs, easing debugging. Engineers can use these insights to tighten model safety and improve reliability.