EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation

• URL: http://arxiv.org/abs/2509.19770v1
• Date: Wed, 24 Sep 2025 05:41:30 GMT
• Title: EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
• Authors: Sen Yang, Yu Bao, Yu Lu, Jiajun Chen, Shujian Huang, Shanbo Cheng,
• Abstract summary: Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation.<n>This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities.
• Score: 49.3025597941589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs. We release codes, datasets, and model checkpoints at https://github.com/NJUNLP/EAX

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