Summary of Preference Optimization with Multi-sample Comparisons, by Chaoqi Wang et al.
Preference Optimization with Multi-Sample Comparisons
by Chaoqi Wang, Zhuokai Zhao, Chen Zhu, Karthik Abinav Sankararaman, Michal Valko, Xuefei Cao, Zhaorun Chen, Madian Khabsa, Yuxin Chen, Hao Ma, Sinong Wang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel post-training approach called Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO) is introduced to optimize generative models. These methods extend current single-sample comparison-based approaches by incorporating multi-sample comparisons, which improve traditional methods like direct alignment from preference methods (DAP). The new approach focuses on group-wise characteristics, enhancing optimization of collective features such as diversity and bias in generative models. Empirical results demonstrate that multi-sample comparisons outperform single-sample comparisons in optimizing these characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models are getting better at creating realistic text and images. But how do we make sure they’re not biased or unfair? The current way of improving these models is by looking at one example at a time, but this might not be enough. We came up with two new ways to improve generative models: mDPO and mIPO. These methods look at multiple examples together to make sure the model is creating diverse and unbiased results. Our tests show that this new approach works better than looking at one example at a time, especially when there’s noise in the data. |
Keywords
» Artificial intelligence » Alignment » Optimization
