Research
153d agoScaling Interpretability for LLMs
Interpretability research for large language models is gaining global traction, as teams worldwide seek to demystify AI decisions. By combining feature, data, and mechanistic attribution, scholars can trace predictions back to inputs, training examples, and internal circuits.
The Signal
Teams at OpenAI and BAIR lead this multi‑lens approach, promising safer, more trustworthy systems that influence policy, industry, and academic collaborations across continents.