TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
Zhaopeng Feng 1 Yan Zhang 2∗ Hao Li22{}^{~{}2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT Bei Wu22{}^{~{}2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT Jiayu Liao22{}^{~{}2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT Wenqiang Liu22{}^{~{}2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT Jun Lang22{}^{~{}2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT Yang Feng33{}^{~{}3}start_FLOATSUPERSCRIPT 3 end_FLOATSUPERSCRIPT Jian Wu11{}^{~{}1}start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT Zuozhu Liu 1 1Zhejiang University 2Tencent 3Angelalign Technology Inc. Equally Contributed.Corresponding author.
Abstract
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-refinement and result in improved translation performance. Motivated by these insights, we introduce a systematic LLM-based self-refinement translation framework, named TEaR, which stands for Translate, Estimate, and Refine, marking a significant step forward in this direction. Our findings demonstrate that 1) our self-refinement 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) TEaR exhibits superior systematicity and interpretability; 3) different estimation strategies yield varied impacts, directly affecting the effectiveness of the final corrections. Additionally, traditional neural translation models and evaluation models operate separately, often focusing on singular tasks due to their limited capabilities
中文速览
大语言模型虽然已经能做出高水平机器翻译，但译文仍常有遗漏、误译和表达不自然等问题，而且普通的自动润色缺少明确的错误判断和反馈。TEaR（Translate、Estimate、Refine）让同一个大模型先翻译，再按多维度翻译质量标准评估错误，最后依据具体反馈修改译文。实验覆盖10种语言、17个翻译方向和多种模型，结果显示TEaR在高低资源语言及不同语言组合上都稳定提升质量，整体优于多种已有后编辑方法，也更受人工评审偏好，其中错误评估反馈的准确性是效果好坏的关键。它把翻译、评价和修改串成了一个透明可解释的闭环，说明通用大模型不仅能翻译，还能借助自我检查有效改进译文，为低成本提升多语种机器翻译提供了实用思路。
原文 arXiv:2402.16379;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2402.16379v3