What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data

Preference data is widely used for aligning language models, but remains largely opaque. While prior work has studied specific aspects of annotator preference (e.g., length or sycophancy), automatically inferring preferences without pre-specifying hypotheses remains challenging. We introduce What's In My Human Feedback (WIMHF), a method that produces human-interpretable, natural language features from preference data using sparse autoencoders. We show that a sparse set of interpretable features can account for two-thirds of the preference signal achieved by black-box models. Applying WIMHF to 7 widely-used datasets, we precisely characterize both (1) which preferences are even possible to measure from each dataset and (2) which preferences humans actually display. WIMHF surfaces preferences that are unintentional or even actively harmful, like a preference for toxic outputs in Chatbot Arena. We show how these findings enable interpretable data curation: re-labeling the examples that contain the harmful preference yields large safety gains (+37%) with no cost to general performance. We also demonstrate a new approach to personalization: on the Community Alignment dataset, we identify preferences that are subjective across annotators, and use the features as interpretable knobs to adjust model behavior along these axes.