Reasoning about In-Context Samples for Machine-Translation
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Computer Science > Computation and Language
Title:Reasoning about In-Context Samples for Machine-Translation
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.27036 [cs.CL] |
| (or arXiv:2608.27036v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27036
arXiv-issued DOI via DataCite (pending registration)
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