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LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic

Deep Papers

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This chapter explores the importance of understanding what happens inside language models (LM) and the concept of mechanistic interpretability. Discussions include the mechanics of interpretability, the use of scaffolding to map features, and the significance of interpretability for ensuring safe AI and exploring practical applications.

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