@inproceedings{
furuta2024geometricaveraged,
title={Geometric-Averaged Preference Optimization for Soft Preference Labels},
author={Hiroki Furuta and Kuang-Huei Lee and Shixiang Shane Gu and Yutaka Matsuo and Aleksandra Faust and Heiga Zen and Izzeddin Gur},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3HpCVZV9it}
}
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.
Geometric-Averaged Preference Optimization for Soft Preference Labels
Hiroki Furuta Kuang-Huei Lee Shixiang Shane Gu Yutaka Matsuo Aleksandra Faust Heiga Zen Izzeddin Gur