The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Learning Transformer Programs with Dan Friedman - #667
Jan 15, 2024
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.
AI Snips
Chapters
Transcript
Episode notes
Mechanistic Interpretability
- Mechanistic interpretability aims to reverse-engineer neural networks into human-understandable algorithms.
- This approach helps understand how models process information, going beyond just observing input-output relationships.
Limitations of Prior Approaches
- Prior interpretability methods, like feature importance, offer hints about model behavior.
- However, they lack the algorithmic understanding needed to predict model actions on new examples.
Inspiration for LTP
- Dan Friedman's approach was inspired by the concept of inherently interpretable models and the RASP programming language.
- RASP allows writing programs that compile into transformer networks, offering a way to link programs and models.