SWEPO: Simultaneous Weighted Preference Optimization for Group Contrastive Alignment

• URL: http://arxiv.org/abs/2412.04628v2
• Date: Wed, 08 Jan 2025 15:00:39 GMT
• Title: SWEPO: Simultaneous Weighted Preference Optimization for Group Contrastive Alignment
• Authors: Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan,
• Abstract summary: We introduce Simultaneous Weighted Preference Optimization (SWEPO), a novel extension of Direct Preference Optimization (DPO) SWEPO accommodate multiple dynamically chosen positive and negative responses for each query. We show that simultaneously considering multiple preferences reduces alignment bias, resulting in more robust alignment. Empirical validation on the UltraFeedback dataset establishes SWEPO as state-of-the-art, with superior performance in downstream evaluations.
• Score: 16.230186347702737
• License:
• Abstract: We introduce Simultaneous Weighted Preference Optimization (SWEPO), a novel extension of Direct Preference Optimization (DPO) designed to accommodate multiple dynamically chosen positive and negative responses for each query. SWEPO employs a weighted group contrastive loss, assigning weights to responses based on their deviation from the mean reward score. This approach effectively prioritizes responses that are significantly better or worse than the average, enhancing optimization. Our theoretical analysis demonstrates that simultaneously considering multiple preferences reduces alignment bias, resulting in more robust alignment. Additionally, we provide insights into the training dynamics of our loss function and a related function, InfoNCA. Empirical validation on the UltraFeedback dataset establishes SWEPO as state-of-the-art, with superior performance in downstream evaluations using the AlpacaEval dataset.

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