Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach

Zhihao Lin, Ziqi Zhu, Hao Huang, Guanghui Wang, Peiyang He

Keywords: Literary Translation, Data Generation, Model Fine-tuning, Large Language Model

TL;DR: We introduce a multi-aspect data generation pipeline for literary translation, leverage the generated data for SFT and RL (finding explicit reward modeling with GRPO outperforms DPO), and train LitMT-8B/14B models that match frontier LLM performance.

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).

Submission Type: Emerging

Submission Number: 83