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English(EN) Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation

新的TITAN框架增强了LLM在低资源多语言翻译方面的能力

研究人员开发了一个名为TITAN的新框架,用于理解和改进多语言大型语言模型(LLM)的翻译能力,特别是在低资源语言方面。该框架利用因果干预和定向优化来识别和优化模型内的特定翻译电路,更新的参数不到5%。实验证明,在印地语到乌尔都语和印地语到信德语的翻译中,BLEU分数有了显著提高,人类评估也证实了其充分性和流畅性的增强。研究还表明,多语言翻译依赖于稀疏、结构化的电路,而不是以英语为中心的枢纽。 AI

影响 这项研究提供了一种参数高效的方法来改进LLM在低资源语言上的翻译能力,有可能使这些模型对更广泛的语言更加可用和有效。

排序理由 该集群包含一篇学术论文,详细介绍了改进LLM翻译能力的新机制框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的TITAN框架增强了LLM在低资源多语言翻译方面的能力

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该集群包含一篇学术论文,详细介绍了改进LLM翻译能力的新机制框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Atai Waris Khan, Abdullah Mazhar, Raghu Kumar, Md Shad Akhtar ·

    低资源多语言翻译的翻译电路发现与定向优化

    arXiv:2610.21456v1 Announce Type: new Abstract: Recent multilingual large language models achieve strong translation performance, yet the internal mechanisms responsible for low-resource multilingual translation remain insufficiently understood. In this paper, we study translatio…