针对机器翻译的上下文示例推理
Reasoning about In-Context Samples for Machine-Translation
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中文总结 AI 辅助
本研究提出基于片段的推理框架，结合Qwen3模型家族在6种语言（每语言最多5领域）的实验，验证其机器翻译性能优于标准k-shot等方法。
中文摘要 AI 辅助
大型语言模型（LLMs）可通过训练实现思维链推理，以提升输出可靠性。本研究探讨如何将显式推理与上下文示例结合，应用于基于LLM的机器翻译（MT）。我们提出一种新颖的基于片段的推理框架：模型先从检索到的相似示例中抽取源-目标平行片段，将其作为中间推理线索生成最终翻译。为训练该模型，我们从大型教师模型中蒸馏出银色片段与草稿。针对Qwen3模型家族、覆盖6种语言（每种语言最多5个领域）的实验表明，基于片段的机器翻译性能显著优于标准k-shot、基础草稿等替代方法。
英文摘要
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.
发表机构
- SYSTRAN by ChapsVision
- Sorbonne Université（索邦大学）
机构由 AI 辅助整理，请以论文原文为准。
