Building Accurate Translation-Tailored LLMs with Language Aware
  Instruction Tuning
Changtong Zan Liang Ding Li Shen Yibing Zhen Weifeng Liu Dacheng Tao
Translation-tailored Large language models (LLMs) exhibit remarkable
translation capabilities, even competing with supervised-trained commercial
translation systems. However, off-target translation remains an unsolved
problem, especially for low-resource languages, hindering us from developing
accurate LLMs-based translation models. To mitigate the off-target translation
problem and enhance the performance of LLMs on translation, recent works have
either designed advanced prompting strategies to highlight the functionality of
translation instructions or exploited the in-context learning ability of LLMs
by feeding few-shot demonstrations. However, these methods essentially do not
improve LLM's ability to follow translation instructions, especially the
language direction information. In this work, we design a two-stage fine-tuning
algorithm to improve the instruction-following ability (especially the
translation direction) of LLMs. Specifically, we first tune LLMs with the
maximum likelihood estimation loss on the translation dataset to elicit the
basic translation capabilities. In the second stage, we construct
instruction-conflicting samples by randomly replacing the translation
directions with a wrong one within the instruction, and then introduce an extra
unlikelihood loss to learn those samples. Experiments on IWSLT and WMT
benchmarks upon the LLaMA model spanning 16 zero-shot directions show that,
compared to the competitive baseline -- translation-finetuned LLama, our method
could effectively reduce the off-target translation ratio (averagely -53.3\%),
thus improving translation quality with average +5.7 SacreBLEU and +16.4
BLEURT. Analysis shows that our method could preserve the model's general task
performance on AlpacaEval. Code and models will be released at
\url{https://github.com/alphadl/LanguageAware_Tuning}.