Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, careful evaluations by human reveal that the translations produced by LLMs still contain multiple errors. Importantly, feeding back such error information into the LLMs can lead to self-correction and result in improved translation performance. Motivated by these insights, we introduce a systematic LLM-based self-correcting translation framework, named TER, which stands for Translate, Estimate, and Refine, marking a significant step forward in this direction. Our findings demonstrate that 1) our self-correction framework successfully assists LLMs in improving their translation quality across a wide range of languages, whether it's from high-resource languages to low-resource ones or whether it's English-centric or centered around other languages; 2) TER exhibits superior systematicity and interpretability compared to previous methods; 3) different estimation strategies yield varied impacts on AI feedback, directly affecting the effectiveness of the final corrections. We further compare different LLMs and conduct various experiments involving self-correction and cross-model correction to investigate the potential relationship between the translation and evaluation capabilities of LLMs.

使用大型语言模型的自我纠正翻译框架TER，在不同语言、高资源和低资源语言之间实现了大幅度的翻译质量提升，并比较了不同的模型和估计策略对翻译质量的影响，同时探究了语言模型的评估能力与翻译质量之间的潜在关系。

系统自我修正改进的基于LLM的机器翻译