Summary of Swepo: Simultaneous Weighted Preference Optimization For Group Contrastive Alignment, by Taneesh Gupta et al.
SWEPO: Simultaneous Weighted Preference Optimization for Group Contrastive Alignment
by Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes Simultaneous Weighted Preference Optimization (SWEPO), a novel approach for aligning large language models with human preferences. SWEPO builds upon Direct Preference Optimization (DPO) by incorporating multiple responses per query and prioritizing those that deviate most from the average reward. This deviation-based weighting enables the model to focus on the most informative outliers, similar to a built-in curriculum. Theoretical analysis shows that multi-preference sampling reduces alignment bias, with an expected deviation bound of O(1/√k). Empirical results demonstrate SWEPO’s superiority over state-of-the-art baselines on Ultra-Feedback and AlpacaEval datasets, achieving up to 4% boosts in length-controlled win-rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to understand what people like or dislike. Right now, we have ways to do this, but they only work with two choices at a time. This paper introduces a new method called Simultaneous Weighted Preference Optimization (SWEPO) that can handle many responses at once. SWEPO helps the computer focus on the most important information by looking for things that are different from what it already knows. The researchers showed that this approach works better than others and can even learn faster. |
Keywords
» Artificial intelligence » Alignment » Optimization
