  Open-sourced large language models (LLMs) have demonstrated remarkable
efficacy in various tasks with instruction tuning. However, these models can
sometimes struggle with tasks that require more specialized knowledge such as
translation. One possible reason for such deficiency is that instruction tuning
aims to generate fluent and coherent text that continues from a given
instruction without being constrained by any task-specific requirements.
Moreover, it can be more challenging for tuning smaller LLMs with lower-quality
training data. To address this issue, we propose a novel framework using
examples in comparison to teach LLMs to learn translation. Our approach
involves presenting the model with examples of correct and incorrect
translations and using a preference loss to guide the model's learning. We
evaluate our method on WMT2022 test sets and show that it outperforms existing
methods. Our findings offer a new perspective on fine-tuning LLMs for
translation tasks and provide a promising solution for generating high-quality
translations. Please refer to Github for more details:
https://github.com/lemon0830/TIM.
