Diagnose-Then-Optimize: A Two-Stage Framework for Error-Aware and Preference-Aligned Machine Translation

While Large Language Models (LLMs) have shown promising capabilities in machine translation, their outputs often lack controllability and the ability to leverage error correction for translation improvement. To address this, we propose a two-stage framework, Diagnose-Then-Optimize (DTO), for structured translation quality enhancement. In the first stage, we fine-tune the large language model using human-annotated error data, enabling it to leverage translation error information for translation correction. In the second stage, we construct a preference dataset using response comparisons evaluated by ChatGPT, focusing on error correctness, correction effectiveness. We apply Direct Preference Optimization (DPO) to refine the model’s output behaviors based on these preferences. Our method demonstrates strong post-editing capabilities, consistently improving translation quality across WMT23 different systems’ outputs. The most significant gains are observed in English-Chinese, highlighting the model’s effectiveness in correcting diverse and complex translation errors. Experiments on WMT23 datasets across English–German, English–Russian, and English–Chinese demonstrate that DTO consistently improves the base LLaMA-3-8B, outperforming large-scale machine translation models such as NLLB_Greedy and Aya-23-35B in COMET scores. Our results highlight the effectiveness of combining structured error supervision with preference-driven fine-tuning, offering a robust and interpretable solution for controllable translation correction.