Preference Optimization with Multi-Sample Comparisons

• URL: http://arxiv.org/abs/2410.12138v1
• Date: Wed, 16 Oct 2024 00:59:19 GMT
• Title: Preference Optimization with Multi-Sample Comparisons
• Authors: Chaoqi Wang, Zhuokai Zhao, Chen Zhu, Karthik Abinav Sankararaman, Michal Valko, Xuefei Cao, Zhaorun Chen, Madian Khabsa, Yuxin Chen, Hao Ma, Sinong Wang,
• Abstract summary: We introduce a novel approach that extends post-training to include multi-sample comparisons. These approaches fail to capture critical characteristics such as generative diversity and bias. We demonstrate that multi-sample comparison is more effective in optimizing collective characteristics than single-sample comparison.
• Score: 53.02717574375549
• License:
• Abstract: Recent advancements in generative models, particularly large language models (LLMs) and diffusion models, have been driven by extensive pretraining on large datasets followed by post-training. However, current post-training methods such as reinforcement learning from human feedback (RLHF) and direct alignment from preference methods (DAP) primarily utilize single-sample comparisons. These approaches often fail to capture critical characteristics such as generative diversity and bias, which are more accurately assessed through multiple samples. To address these limitations, we introduce a novel approach that extends post-training to include multi-sample comparisons. To achieve this, we propose Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO). These methods improve traditional DAP methods by focusing on group-wise characteristics. Empirically, we demonstrate that multi-sample comparison is more effective in optimizing collective characteristics~(e.g., diversity and bias) for generative models than single-sample comparison. Additionally, our findings suggest that multi-sample comparisons provide a more robust optimization framework, particularly for dataset with label noise.

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