Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data

• URL: http://arxiv.org/abs/2504.14669v1
• Date: Sun, 20 Apr 2025 16:20:30 GMT
• Title: Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data
• Authors: Wei Zou, Sen Yang, Yu Bao, Shujian Huang, Jiajun Chen, Shanbo Cheng,
• Abstract summary: We propose a self-play framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs)<n>Experiments demonstrate that this approach not only matches the performance of models trained on large-scale parallel data but also excels in non-English translation directions.
• Score: 64.4458540273004
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rise of Large Language Models (LLMs) has reshaped machine translation (MT), but multilingual MT still relies heavily on parallel data for supervised fine-tuning (SFT), facing challenges like data scarcity for low-resource languages and catastrophic forgetting. To address these issues, we propose TRANS-ZERO, a self-play framework that leverages only monolingual data and the intrinsic multilingual knowledge of LLM. TRANS-ZERO combines Genetic Monte-Carlo Tree Search (G-MCTS) with preference optimization, achieving strong translation performance that rivals supervised methods. Experiments demonstrate that this approach not only matches the performance of models trained on large-scale parallel data but also excels in non-English translation directions. Further analysis reveals that G-MCTS itself significantly enhances translation quality by exploring semantically consistent candidates through iterative translations, providing a robust foundation for the framework's succuss.

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