  Large language models (LLMs) have exhibited remarkable performance in various
natural language processing tasks. Techniques like instruction tuning have
effectively enhanced the proficiency of LLMs in the downstream task of machine
translation. However, the existing approaches fail to yield satisfactory
translation outputs that match the quality of supervised neural machine
translation (NMT) systems. One plausible explanation for this discrepancy is
that the straightforward prompts employed in these methodologies are unable to
fully exploit the acquired instruction-following capabilities. To this end, we
propose the TasTe framework, which stands for translating through
self-reflection. The self-reflection process includes two stages of inference.
In the first stage, LLMs are instructed to generate preliminary translations
and conduct self-assessments on these translations simultaneously. In the
second stage, LLMs are tasked to refine these preliminary translations
according to the evaluation results. The evaluation results in four language
directions on the WMT22 benchmark reveal the effectiveness of our approach
compared to existing methods. Our work presents a promising approach to unleash
the potential of LLMs and enhance their capabilities in MT. The codes and
datasets are open-sourced at https://github.com/YutongWang1216/ReflectionLLMMT.
