Tuning LLMs with Contrastive Alignment Instructions for Machine
  Translation in Unseen, Low-resource Languages
Zhuoyuan Mao Yen Yu
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.