
LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic
Deep Papers
Exploring the Importance of Intermediary Steps and Features in Machine Learning Models
This chapter emphasizes the significance of delving into intermediary steps within machine learning models to gain insights into their functionality, utilizing techniques like feature ablations to gauge the influence of individual components. It also explores the exploration of model features through approaches such as single prompts and geometric methods.
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