解锁大语言模型在机器翻译中的推理能力 / Unlocking Reasoning Capability on Machine Translation in Large Language Models
1️⃣ 一句话总结
这篇论文研究发现，当前大语言模型通用的‘显式推理’方法（即写出中间思考步骤）会损害机器翻译质量，并提出了一种专为翻译任务设计的‘结构化推理’框架，通过多步骤草拟、优化和选择性修订，显著提升了翻译效果。
arXiv 提交日期: 2026-02-16
Abstract - Unlocking Reasoning Capability on Machine Translation in Large Language Models
Reasoning-oriented large language models (RLMs) achieve strong gains on tasks such as mathematics and coding by generating explicit intermediate reasoning. However, their impact on machine translation (MT) remains underexplored. We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models. Analysis reveals that MT reasoning traces are highly linear, lacking revision, self-correction and exploration of alternative translations, which limits their usefulness. Furthermore, injecting higher-quality reasoning traces from stronger models does not reliably improve weaker models' performance. To address this mismatch, we propose a structured reasoning framework tailored to translation, based on multi-step drafting, adequacy refinement, fluency improvement, and selective iterative revision. We curate a synthetic dataset of dynamic structured reasoning traces and post-train a large reasoning model on this data. Experiments show significant improvements over standard translation fine-tuning and injected generic reasoning baselines. Our findings demonstrate that reasoning must be task-structured to benefit MT.
源自 arXiv: 2602.14763