AINeutralarXiv – CS AI · 14h ago7/10
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What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
Researchers introduce WIMHF, a method using sparse autoencoders to decode what human feedback datasets actually measure and express about AI model preferences. The technique identifies interpretable features across 7 datasets, revealing diverse preference patterns and uncovering potentially unsafe biases—such as LMArena users voting against safety refusals—while enabling targeted data curation that improved safety by 37%.