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LLM-Driven Feature Discovery For Model Behavior
A new exploratory project uses black-box LLM autoraters to extract 10-20 qualitative features from model transcripts. The process splits data into user turns, thoughts, and responses to identify novel behaviors. This method helps AI Alignment researchers find surprising correlations in deployment distributions. It offers a scalable way to audit target models without manual review.