@inproceedings{zhang-etal-2025-cm,
    title = "{CM}-Align: Consistency-based Multilingual Alignment for Large Language Models",
    author = "Zhang, Xue  and
      Liang, Yunlong  and
      Meng, Fandong  and
      Zhang, Songming  and
      Chen, Yufeng  and
      Xu, Jinan  and
      Zhou, Jie",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1401/",
    doi = "10.18653/v1/2025.findings-emnlp.1401",
    pages = "25689--25702",
    ISBN = "979-8-89176-335-7",
    abstract = "Current large language models (LLMs) generally show a significant performance gap in alignment between English and other languages.To bridge this gap, existing research typically leverages the model{'}s responses in English as a reference to select the best/worst responses in other languages, which are then used for Direct Preference Optimization (DPO) training.However, we argue that there are two limitations in the current methods that result in noisy multilingual preference data and further limited alignment performance: 1) Not all English responses are of high quality, and using a response with low quality may mislead the alignment for other languages. 2) Current methods usually use biased or heuristic approaches to construct multilingual preference pairs.To address these limitations, we design a consistency-based data selection method to construct high-quality multilingual preference data for improving multilingual alignment (CM-Align).Specifically, our method includes two parts: consistency-guided English reference selection and cross-lingual consistency-based multilingual preference data construction.Experimental results on three LLMs and three common tasks demonstrate the effectiveness and superiority of our method, which further indicates the necessity of constructing high-quality preference data."
}