dailyai.report

A new exploratory project uses black box LLMs to identify 10-20 distinct features within model transcripts. The method splits data into user turns, thoughts, and responses to uncover hidden correlations. This automated labeling helps researchers find novel behaviors in deployment and RL training. It provides a scalable way to audit target model qualitative behaviors.