Tuning LLM s with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages (2024)

Abstract: 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.

## introduction large language models (llms) @xcite @xcite @xcite @xcite achieved good performance for a wide range of nlp tasks for prevalent languages. however, insufficient coverage for low-resource languages remains to be one significant limitation. low-resource languages are either not present, or orders of magnitude smaller in size than dominant languages in the pre-training dataset. this limitation is in part due to the prohibitive cost incurred by curating good quality and adequately sized datasets for pre-training. incrementally adapting existing multilingual llms to incorporate an unseen, low-resource language thus becomes a costeffective priority to address this limitation. previous study (de la @xcite @xcite explored extending language support using either continual pre-training @xcite @xcite , or parameter efficient fine-tuning (peft) methods @xcite @xcite on monolingual tasks. extending language support for cross-lingual tasks remains underexplored due to the challenge of incrementally inducing cross-lingual understanding and generation abilities in llms @xcite . this study focused on machine translation (mt) to highlight the cross-lingual llm adaptation challenge. the challenge lies in enabling translation for low-resource languages that often lack robust cross-lingual signals. we first explored the efficacy of fine-tuning llms with mt instructions (mtinstruct) in unseen, low-resource languages. mtinstruct is a method previously shown to bolster the translation proficiency of llms for sup-1 ported languages @xcite . subsequently, given that cross-lingual alignments are suboptimal in llms as a result of data scarcity of lowresource languages, we proposed contrastive alignment instructions (aligninstruct) to explicitly provide cross-lingual supervision during mt finetuning. aligninstruct is a cross-lingual discriminator formulated using statistical word alignments. our approach was inspired by prior studies @xcite @xcite , which indicated the utility of word alignments in enhancing mt. in addition to aligninstruct, we discussed two word-level crosslingual instruction alternatives cast as generative tasks for comparison with aligninstruct. our experiments fine-tuned the bloomz models @xcite of varying sizes (1b1, 3b, and 7b1) for 24 unseen, low-resource languages, and evaluated translation on opus-100 @xcite and @xcite . we first showed that mtinstruct effectively induced the translation capabilities of llms for these languages. building on the mtinstruct baseline, the multi-task learning combining aligninstruct and mtinstruct resulted in stronger translation performance without the need for additional training corpora. the performance improved with larger bloomz models, as illustrated in fig. 1 , indicating that aligninstruct is particularly beneficial for larger llms during mt fine-tuning. when compared with the generative variants of aligninstruct, our results indicated that discriminative instructions better complemented mtinstruct. furthermore, merging aligninstruct with its generative counterparts did not further improve translation quality, underscoring the efficacy and sufficiency of aligninstruct in leveraging word alignments for mt. in zero-shot translation evaluation on the opus benchmark, aligninstruct exhibited improvements over the mtinstruct baseline in 30 zero-shot directions between non-english languages, when exclusively fine-tuned with three unseen languages @xcite . however, when incorporating supported languages (arabic, french, and chinese) the benefits of aligninstruct were only evident in zero-shot translations where the target language was a supported language. in addition, to interpret the inherent modifications within the bloomz models after applying mtinstruct or aligninstruct, we conducted a visualization of the layer-wise cross-lingual alignment capabilities of the model representations. ## methodology this section presents mtinstruct as the baseline, and aligninstruct. the mtinstruct baseline involved fine-tuning llms using mt instructions. aligninstruct dealt with the lack of cross-lingual signals stemming from the limited parallel training data in low-resource languages. the expectation was enhanced cross-lingual supervision cast as a discriminative task without extra training corpora. following this, we introduced two generative variants of aligninstruct for comparison. foot_0 ## evaluation and analysis this section reports bleu @xcite , chrf++ @xcite , and comet @xcite foot_8 scores for respective experimental configurations. we further character-ized of the degree to which intermediate embeddings were language-agnostic after fine-tuning. ## conclusion in this study, we introduced aligninstruct for enhancing the fine-tuning of llms for mt in unseen, low-resource languages while limiting the use of additional training corpora. our multilingual and zero-shot findings demonstrated the strength of aligninstruct over the mtinstruct baseline and other instruction variants. our future work pertains to exploring using large monolingual corpora of unseen languages for mt and refining the model capability to generalize across diverse mt prompts.