CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909897626484736 |
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| author | Pokharel, Rhitabrat Tao, Yufei Agrawal, Ameeta |
| author_facet | Pokharel, Rhitabrat Tao, Yufei Agrawal, Ameeta |
| contents | Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet effective alternative, Confidence-Aware Preference Optimization (CAPO), which replaces DPO's fixed treatment of preference pairs with a dynamic loss scaling mechanism based on a relative reward. By modulating the learning signal according to the confidence in each preference pair, CAPO enhances robustness to noisy or low-margin comparisons, typically encountered in multilingual text. Empirically, CAPO outperforms existing preference optimization baselines by at least 16% in reward accuracy, and improves alignment by widening the gap between preferred and dispreferred responses across languages. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_07691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences Pokharel, Rhitabrat Tao, Yufei Agrawal, Ameeta Computation and Language Artificial Intelligence Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet effective alternative, Confidence-Aware Preference Optimization (CAPO), which replaces DPO's fixed treatment of preference pairs with a dynamic loss scaling mechanism based on a relative reward. By modulating the learning signal according to the confidence in each preference pair, CAPO enhances robustness to noisy or low-margin comparisons, typically encountered in multilingual text. Empirically, CAPO outperforms existing preference optimization baselines by at least 16% in reward accuracy, and improves alignment by widening the gap between preferred and dispreferred responses across languages. |
| title | CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07691 |