用分块翻译提升低资源机器翻译效果，让大模型先拆句再找相似短语翻译。
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
- 将句子拆成短语，用检索到的相似短语对进行翻译
- 在FLORES 200等基准上显著提升翻译性能
- 特别适合低资源或领域外数据场景
生成式大语言模型（LLM）具备上下文学习能力，使其在自然语言处理任务中表现出色。机器翻译可通过提供与目标句语义相近的示例来提升效果。本文提出一种新型基于LLM的翻译范式——组合翻译，替代传统的简单少样本翻译。该方法利用LLM将句子分解为更简单的短语，并借助检索到的示范对分别翻译每个短语。最后，通过提示模型结合自生成的短语-翻译对完成原句翻译。其核心思路是：较短的短语更易翻译且更易匹配相关示例。该方法在低资源场景下尤为有效，尤其当可选示例池较小或数据域外时。实验表明，组合翻译在多个主流机器翻译基准（包括FLORES 200、NTREX 128和TICO-19）上显著提升性能。代码与输出已开源于https://github.com/ArmelRandy/compositional-translation。
原文摘要 · Abstract (English)
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. Machine Translation (MT) has been shown to benefit from in-context examples, in particular when they are semantically similar to the sentence to translate. In this paper, we propose a new LLM-based translation paradigm, compositional translation, to replace naive few-shot MT with similarity-based demonstrations. An LLM is used to decompose a sentence into simpler phrases, and then to translate each phrase with the help of retrieved demonstrations. Finally, the LLM is prompted to translate the initial sentence with the help of the self-generated phrase-translation pairs. Our intuition is that this approach should improve translation because these shorter phrases should be intrinsically easier to translate and easier to match with relevant examples. This is especially beneficial in low-resource scenarios, and more generally whenever the selection pool is small or out of domain. We show that compositional translation boosts LLM translation performance on a wide range of popular MT benchmarks, including FLORES 200, NTREX 128 and TICO-19. Code and outputs are available at https://github.com/ArmelRandy/compositional-translation
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