Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Machine Translation
In-Context Samples
Reasoning
Reasoning about In-Context Samples for Machine-Translation
🤖 AI Summary
研究通过引入基于片段的推理框架，利用从相似示例中提取的源-目标片段作为中间推理痕迹，以提高大语言模型在机器翻译中的表现。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
fragment-based reasoning
in-context samples
machine translation
silver fragments
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