Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
CoRR(2024)
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 pretrained multilingual large language
models (LLMs) with high-quality translation pairs. In this paper, we focus on
boosting the many-to-many multilingual translation performance of LLMs with an
emphasis on zero-shot translation directions. We demonstrate that prompt
strategies adopted during instruction finetuning are crucial to zero-shot
translation performance 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
multilingual finetuning on LLMs with translation instructions. Experimental
results on ALMA (Xu et al., 2023) 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.
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