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This study addresses the scarcity of high-quality literary translation data by using specialized LLMs to generate synthetic references that outperform original ground truth for supervised fine-tuning. The approach reveals that while Direct Preference Optimization degrades performance in this domain, an explicit reward model within the GRPO framework yields measurable gains. The resulting models demonstrate competitive benchmark scores against top-tier commercial offerings while maintaining strong generalization to out-of-domain texts.
Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach
arXiv:2606.05924v1 Announce Type: cross Abstract: Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect. We present a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators, each targeting a distinct quality dimension. We leverage the generated data for supervised fine-tuning and reinforcement learning. Experiments show that our generated references outperform the original ground truth for SFT by 8.65 CEA100 points. For reinforcement learning, we find that DPO leads to performance degradation in this setting, while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. We attribute this to the stability of two-stage training and GRPO's online exploration capability. Our resulting models, LitMT-8B and LitMT-14B, achieve 67.25 and 69.07 CEA100 respectively on the MetaphorTrans English-to-Chinese literary translation benchmark, competitive with Claude Sonnet 4.5 at 68.43, and demonstrate strong generalization to out-of-domain literary work (i.e., O. Henry).
Abstract:Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect. We present a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators, each targeting a distinct quality dimension. We leverage the generated data for supervised fine-tuning and reinforcement learning. Experiments show that our generated references outperform the original ground truth for SFT by 8.65 CEA100 points. For reinforcement learning, we find that DPO leads to performance degradation in this setting, while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. We attribute this to the stability of two-stage training and GRPO's online exploration capability. Our resulting models, LitMT-8B and LitMT-14B, achieve 67.25 and 69.07 CEA100 respectively on the MetaphorTrans English-to-Chinese literary translation benchmark, competitive with Claude Sonnet 4.5 at 68.43, and demonstrate strong generalization to out-of-domain literary work (i.e., O. Henry).
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.05924 [cs.CL]
(or arXiv:2606.05924v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05924 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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