利用对比对齐指令调整LLMs，用于未见过的、低资源语言的机器翻译

Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages

January 11, 2024

作者: Zhuoyuan Mao, Yen Yu

cs.AI

摘要

本文介绍了对抗对齐指导（AlignInstruct）来解决大型语言模型（LLMs）机器翻译（MT）中的两个挑战。一个是将支持的语言扩展到以前未见过的语言。第二个与低资源语言中数据不足有关。通过MT指导（MTInstruct）对模型进行微调是解决第一个挑战的一种直接方法。然而，MTInstruct受到第二个挑战中固有的弱跨语言信号的限制。AlignInstruct强调通过使用基于统计词对齐构建的跨语言鉴别器的跨语言监督。我们基于对BLOOMZ模型（1b1、3b和7b1）进行微调的结果，展示了在多达24种未见过的语言中：（1）LLMs可以使用MTInstruct有效地翻译未见过的语言；（2）AlignInstruct在涉及英语的48个翻译方向中导致翻译质量的一致提升；（3）基于鉴别器的指导在跨语言指导中优于生成对应物；（4）AlignInstruct提高了30个零翻译方向的性能。

English

This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a straightforward approach to the first challenge. However, MTInstruct is limited by weak cross-lingual signals inherent in the second challenge. AlignInstruct emphasizes cross-lingual supervision via a cross-lingual discriminator built using statistical word alignments. Our results based on fine-tuning the BLOOMZ models (1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can effectively translate unseen languages using MTInstruct; (2) AlignInstruct led to consistent improvements in translation quality across 48 translation directions involving English; (3) Discriminator-based instructions outperformed their generative counterparts as cross-lingual instructions; (4) AlignInstruct improved performance in 30 zero-shot directions.