Geometric-Averaged Preference Optimization for Soft Preference Labels
This work addresses the challenge of representing variable human preferences in LLM alignment, offering an incremental improvement to mitigate over-optimization and objective mismatch in existing methods.
The authors tackled the problem of aligning LLMs with human preferences by addressing the assumption that preferences are binary and deterministic, proposing distributional soft preference labels and improving DPO with a weighted geometric average in the loss function, resulting in more preferable responses than binary labels and significant improvements in cases with modestly-confident labels.
Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. However, human preferences can vary across individuals, and therefore should be represented distributionally. In this work, we introduce the distributional soft preference labels and improve Direct Preference Optimization (DPO) with a weighted geometric average of the LLM output likelihood in the loss function. This approach adjusts the scale of learning loss based on the soft labels such that the loss would approach zero when the responses are closer to equally preferred. This simple modification can be easily applied to any DPO-based methods and mitigate over-optimization and objective mismatch, which prior works suffer from. Our experiments simulate the soft preference labels with AI feedback from LLMs and demonstrate that geometric averaging consistently improves performance on standard benchmarks for alignment research. In particular, we observe more preferable responses than binary labels and significant improvements where modestly-confident labels are in the majority.