  This paper presents a study on strategies to enhance the translation
capabilities of large language models (LLMs) in the context of machine
translation (MT) tasks. The paper proposes a novel paradigm consisting of three
stages: Secondary Pre-training using Extensive Monolingual Data, Continual
Pre-training with Interlinear Text Format Documents, and Leveraging
Source-Language Consistent Instruction for Supervised Fine-Tuning. Previous
research on LLMs focused on various strategies for supervised fine-tuning
(SFT), but their effectiveness has been limited. While traditional machine
translation approaches rely on vast amounts of parallel bilingual data, our
paradigm highlights the importance of using smaller sets of high-quality
bilingual data. We argue that the focus should be on augmenting LLMs'
cross-lingual alignment abilities during pre-training rather than solely
relying on extensive bilingual data during SFT. Experimental results conducted
using the Llama2 model, particularly on Chinese-Llama2 after monolingual
augmentation, demonstrate the improved translation capabilities of LLMs. A
significant contribution of our approach lies in Stage2: Continual Pre-training
with Interlinear Text Format Documents, which requires less than 1B training
data, making our method highly efficient. Additionally, in Stage3, we observed
that setting instructions consistent with the source language benefits the
supervised fine-tuning process. Experimental results demonstrate that our
approach surpasses previous work and achieves superior performance compared to
models such as NLLB-54B and GPT3.5-text-davinci-003, despite having a
significantly smaller parameter count of only 7B or 13B. This achievement
establishes our method as a pioneering strategy in the field of machine
translation.
