Interpretability via Symbolic Distillation

Explore Miles Cranmer's insightful lecture on interpretability through symbolic distillation, delivered as part of the Large Language Models and Transformers series at the Simons Institute. Delve into advanced techniques for enhancing the transparency and understanding of complex machine learning models, with a focus on their application to large language models and transformers. Gain valuable insights into cutting-edge research that aims to bridge the gap between the power of neural networks and the interpretability of symbolic systems.