Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles (2024.findings-naacl)
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Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, Yunmo Chen
| Challenge: | Recent work shows that large language models can generalize to machine translation using zero-shot examples with in-context learning. |
| Approach: | They investigate the factors contributing to this gap by matching the writing styles of the target corpus. |
| Outcome: | The proposed methods can be enhanced without the need for parallel demonstration examples. |
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