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10min chapter
Localizing and Editing Knowledge in LLMs with Peter Hase - #679
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Understanding Model Interpretability and Localization in Machine Learning
This chapter explores the importance of interpretability in machine learning models and how understanding the interpretability domain can improve model accuracy. It focuses on knowledge localization and interpretability in models, touching on the challenges of editing stored facts in neural networks. The discussion delves into techniques like causal tracing for enhancing interpretability in machine learning models.
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Discover
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