Dan Friedman

Dan Friedman, a PhD student from Princeton's NLP group, dives into his fascinating research on mechanistic interpretability for transformer models. He discusses his innovative paper that modifies transformer architecture to create human-readable programs. The conversation uncovers the challenges of current interpretability methods and contrasts them with his approach. They explore the RASP framework's role in transforming programs and delve into the complexities of optimizing model constraints, highlighting the importance of clarity in understanding AI.