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.
38:48
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Quick takeaways
- The LTP paper proposes modifications to the transformer architecture which allow transformer models to be easily converted into human-readable programs, making them inherently interpretable.
- Dan Friedman and his team propose a design constraint for transformers that ensures interpretability by optimizing disentangled residual streams and modular calculation to obtain human-readable representations of the model's decision-making process.
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Research background and interest in interpretability
Dan Friedman is a PhD student in computer science at Princeton University. His research focuses on interpretability for transformers, specifically in the context of natural language processing (NLP). He is interested in understanding how machine learning models process natural language and aims to develop methods for interpreting their decision-making processes.
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