Today, we continue our NeurIPS series with Dan Friedman, a PhD student in the Princeton NLP group. In our conversation, we explore his research on mechanistic interpretability for transformer models, specifically his paper, Learning Transformer Programs. 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. In our conversation, we compare the approach proposed by this research with prior approaches to understanding the models and their shortcomings. We also dig into the approach’s function and scale limitations and constraints.
The complete show notes for this episode can be found at twimlai.com/go/667.
Powering AI with the World's Largest Computer Chip with Joel Hestness - #684
AI for Power & Energy with Laurent Boinot - #683
Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682
GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680
Localizing and Editing Knowledge in LLMs with Peter Hase - #679
Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678
V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677
Video as a Universal Interface for AI Reasoning with Sherry Yang - #676
Assessing the Risks of Open AI Models with Sayash Kapoor - #675
OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674
Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673
Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672
Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671
AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670
Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669
Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668
AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666
AI Trends 2024: Computer Vision with Naila Murray - #665
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