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28min chapter
LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic
Deep Papers
Interpretability in LLMs: Feature Research Perspective
The chapter explores interpretability research in Large Language Models (LLMs) to identify key features and improve scalability, making them accessible to companies. It discusses the potential of interpretability research in enhancing AI human alignment and emotion identification capabilities. The conversation covers topics like decomposing sparse autoencoders, feature clamping, challenges in training models with a large number of features, ongoing research in interpretability, and comparison between research papers from OpenAI and Anthropic.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode