用片段推理提升大模型翻译准确率
Reasoning about In-Context Samples for Machine-Translation
- 从相似例句中提取源-目标片段作为推理中间步骤
- 在6种语言、5个领域上显著优于传统k-shot方法
- 适合需要高精度翻译的场景，如专业文档翻译
大型语言模型可通过链式思维推理提升回答可靠性。本文研究如何利用显式推理改进基于上下文样例的大模型机器翻译。提出一种新的基于片段的推理框架：模型首先从检索到的相似示例中提取平行的源-目标片段，再以这些片段作为中间推理路径生成最终翻译。训练时，通过大规模教师模型提炼银标准片段与草稿。在Qwen3模型族上，覆盖6种语言、每语言最多5个领域，实验表明该方法显著优于标准k-shot或基础草稿法。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can be trained to perform chain-of-thoughts reasoning in order to improve the reliability of their responses. In this work, we investigate how explicit reasoning can be leveraged for LLM-Based Machine Translation (MT) with in-context samples. We introduce a novel fragment-based reasoning framework in which the model first extracts parallel source-target fragments from retrieved similar exemplars, and uses these fragments as intermediate reasoning traces to produce the final translation. To train our model, we distill silver fragments and drafts from a large teacher model. Our experiments with the Qwen3 model family, over 6 languages, including up to 5 domains per language, demonstrate that fragment-based MT significantly outperforms alternative methods like standard k-shot or basic drafting.
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