迈向利用大型语言模型提升多对多多语言机器翻译
Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
AI总结：
本文提出跨语言一致性正则化XConST，通过改进微调提示策略，提升大型语言模型在多对多多语言翻译中的零样本翻译性能。
摘要：
机器翻译的训练范式已逐渐转变，从利用大规模平行语料库学习神经机器翻译（NMT）模型，转向在具有高质量翻译对的多语言大型语言模型（LLMs）上进行指令微调。本文聚焦于提升LLMs的多对多多语言翻译能力，特别强调零样本翻译方向。我们证明了微调过程中采用的提示策略对零样本翻译至关重要，并引入了一种跨语言一致性正则化方法XConST，以弥合不同语言之间的表示差距，提升零样本翻译性能。XConST并非一种新方法，而是CrossConST（Gao等，2023a）针对LLMs翻译指令微调改编的版本。在ALMA（Xu等，2023）、Tower（Team，2024）和LLaMA-2（Touvron等，2023）上的实验结果表明，我们的方法持续提升了翻译性能。我们的实现可在https://github.com/gpengzhi/CrossConST-LLM获取。
英文摘要：
The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning on multilingual large language models (LLMs) with high-quality translation pairs. In this paper, we focus on boosting many-to-many multilingual translation of LLMs with an emphasis on zero-shot translation directions. We demonstrate that prompt strategies adopted during finetuning are crucial to zero-shot translation and introduce a cross-lingual consistency regularization, XConST, to bridge the representation gap among different languages and improve zero-shot translation performance. XConST is not a new method, but a version of CrossConST (Gao et al., 2023a) adapted for translation instruction finetuning with LLMs. Experimental results on ALMA (Xu et al., 2023), Tower (Team, 2024), and LLaMA-2 (Touvron et al., 2023) show that our approach consistently improves translation performance. Our implementations are available at https://github.com/gpengzhi/CrossConST-LLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
