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

arXiv — cs.CLFriday, October 31, 2025 at 4:00:00 AM
A new method called What's In My Human Feedback? (WIMHF) has been introduced to help explain how human feedback influences language models. This is significant because understanding feedback data can lead to better model performance and more predictable outcomes, addressing a key challenge in the field. By using sparse autoencoders, WIMHF aims to automatically extract relevant features from feedback without needing pre-defined hypotheses, which could revolutionize how practitioners approach model training.
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