WIMHF uses sparse autoencoders on seven preference datasets to identify a handful of human-interpretable features that capture the majority of the signal in black-box preference models.
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What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
WIMHF uses sparse autoencoders on seven preference datasets to identify a handful of human-interpretable features that capture the majority of the signal in black-box preference models.