Research
153d agoScaling Interpretability for Large Language Models
Researchers at Bair present a framework that scales interpretability methods to large language models, enabling teams worldwide to trace predictions back to input features, training data, and internal mechanisms.
The Signal
By unifying feature, data, and mechanistic attribution, the approach promises safer, more transparent AI systems across industries, fostering global trust in automated decision‑making.