Reasoning-Guided LLM Translation Optimization: A Framework Using Multidimensional Postediting Feedback

Abstract

While Large Language Models (LLMs) demonstrate strong translation capabilities, optimizing their output towards human-level refinement necessitates reasoning-guided approaches that move beyond simple generation. This paper introduces Multidimensional Feedback and Postedit Thought (MFPE), a novel framework specifically designed for reasoning-guided LLM translation optimization. MFPE operationalizes this guidance by leveraging multidimensional postediting feedback, which acts as explicit reasoning signals to the LLM. This feedback mechanism simulates the human postediting process, where errors are systematically identified and corrected. Generated by a dedicated optimization model trained on a synthetic dataset (using GLM-4 and inspired by multidimensional quality metrics (MQM), this feedback provides fine-grained error details including spans, categories, and quantities from initial LLM translations. We conduct experiments across four language pairs: Chinese-English, German-English, English-Chinese, and English-German. The results show that fine-tuning with structured, reasoning-like feedback significantly enhances translation quality and outperforms standard bilingual fine-tuning approaches. Our findings highlight the effectiveness of simulating postediting reasoning through structured feedback, offering a promising direction for harnessing and improving the inferential capabilities of LLMs for complex tasks like high-quality machine translation.