English(EN) An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages

研究发现：多样例上下文学习可提升低资源语言翻译能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-25 04:00

研究人员对低资源语言的机器翻译进行了多样例上下文学习（ICL）的实证研究。研究结果表明，增加ICL中的示例数量通常能提高性能。研究还表明，使用基于BM25的检索来选择示例可以显著提高数据效率，从而用更少的示例获得可比的结果。此外，研究表明ICL与微调技术结合使用可以带来额外的好处。 AI

影响这项研究可能为目前AI服务不足的语言带来更高效、更有效的机器翻译系统。

排序理由该集群包含一篇学术论文，详细介绍了机器翻译技术的实证研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了机器翻译技术的实证研究结果。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CL TIER_1 English(EN) · Yinhan Lu, Gaganpreet Jhajj, Chen Zhang, Anietie Andy, David Ifeoluwa Adelani · 2026-06-25 04:00

低资源语言机器翻译的多样本上下文内学习的实证研究

arXiv:2604.02596v3 Announce Type: replace Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs …