Compositional preference models for alignment with scalable oversight

Published: 23 Oct 2023, Last Modified: 28 Nov 2023SoLaR SpotlightEveryoneRevisionsBibTeX
Keywords: language model alignment, preference model, Reinforcement Learning from Human Feedback (RLHF), overoptimization, interpretability, scalable oversight, reward hacking
TL;DR: We decompose the preference assessment into interpretable features using a prompted LM and aggregate their scores using a logistic regression classifier.
Abstract: As language models (LMs) become more capable, it is increasingly important to align them with human preferences. However, the dominant paradigm for training Preference Models (PMs) for that purpose suffers from fundamental limitations, such as lack of transparency and scalability, along with susceptibility to overfitting the preference dataset. We propose Compositional Preference Models (CPMs), a novel PM framework that decomposes one global preference assessment into several interpretable features, obtains scalar scores for these features from a prompted LM, and aggregates these scores using a logistic regression classifier. CPMs allow to control which properties of the preference data are used to train the preference model and to build it based on features that are believed to underlie the human preference judgement. Our experiments show that CPMs not only improve interpretability and are more robust to overoptimization than standard PMs, but also that best-of-$n$ samples obtained using CPMs tend to be preferred over samples obtained using conventional PMs. Overall, our approach demonstrates the benefits of endowing PMs with priors about which features determine human preferences while relying on LM capabilities to extract those features in a scalable and interpretable way.
Submission Number: 56