A Paradigm Shift in Machine Translation: Boosting Translation
  Performance of Large Language Models
Authors: name: Haoran Xu, name: Young Jin Kim, name: Amr Sharaf, name: Hany Hassan Awadalla

Generative Large Language Models (LLMs) have achieved remarkable advancements
in various NLP tasks. However, these advances have not been reflected in the
translation task, especially those with moderate model sizes (i.e., 7B or 13B
parameters), which still lag behind conventional supervised encoder-decoder
translation models. Previous studies have attempted to improve the translation
capabilities of these moderate LLMs, but their gains have been limited. In this
study, we propose a novel fine-tuning approach for LLMs that is specifically
designed for the translation task, eliminating the need for the abundant
parallel data that traditional translation models usually depend on. Our
approach consists of two fine-tuning stages: initial fine-tuning on monolingual
data followed by subsequent fine-tuning on a small set of high-quality parallel
data. We introduce the LLM developed through this strategy as Advanced Language
Model-based trAnslator (ALMA). Based on LLaMA-2 as our underlying model, our
results show that the model can achieve an average improvement of more than 12
BLEU and 12 COMET over its zero-shot performance across 10 translation
directions from the WMT'21 (2 directions) and WMT'22 (8 directions) test
datasets. The performance is significantly better than all prior work and even
superior to the NLLB-54B model and GPT-3.5-text-davinci-003, with only 7B or
13B parameters. This method establishes the foundation for a novel training
paradigm in machine translation.