Reasoning about In-Context Samples for Machine-Translation
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 ...
| Authors | Maxime Bouthors, Josep Crego, François Yvon |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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