TRANS-ZERO: Self-Play Incentivizes Large Language Models to Achieve Multilingual Translation Without Parallel Data

ACL ARR 2025 February Submission3483 Authors

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 success.

Paper Type: Long

Research Area: Machine Translation

Research Area Keywords: Machine Translation

Contribution Types: NLP engineering experiment

Languages Studied: English,Italian,German,Portuguese,Chinese,Russian

Submission Number: 3483