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      Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles

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          Abstract

          Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning. However, even though zero-shot translations are relatively good, there remains a discernible gap comparing their performance with the few-shot setting. In this paper, we investigate the factors contributing to this gap and find that this gap can largely be closed (for about 70%) by matching the writing styles of the target corpus. Additionally, we explore potential approaches to enhance zero-shot baselines without the need for parallel demonstration examples, providing valuable insights into how these methods contribute to improving translation metrics.

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          Journal
          03 November 2023
          Article
          2311.02310
          28ff698f-dcf4-4e9e-a131-f5605a039dfd

          http://creativecommons.org/licenses/by/4.0/

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          cs.CL

          Theoretical computer science
          Theoretical computer science

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