xTower：一个用于解释和纠正翻译错误的多语言大语言模型
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
AI总结：
本文提出基于TowerBase的多语言大模型xTower，通过为翻译错误生成自由文本解释来指导译文修正，实验表明其解释合理有用且能显著提升翻译质量。
摘要：
尽管机器翻译（MT）系统在基准测试上取得了越来越强的性能，但它们常常会产生带有错误和异常的译文。理解这些错误可能有助于提高翻译质量和用户体验。本文介绍了xTower，一个基于TowerBase构建的开放大语言模型（LLM），旨在为翻译错误提供自由文本解释，以指导生成修正后的译文。xTower所生成解释的质量通过内在评估和外在评估两方面进行评价。我们邀请专业译员从两个维度评估解释的质量：与所解释错误片段的相关性，以及在理解错误和提高翻译质量方面的帮助性。在外在评估方面，我们在多种实验设置下测试xTower生成翻译修正的能力，结果表明翻译质量有显著提升。我们的发现凸显了xTower的潜力，它不仅能对自动译文给出合理且有用的解释，还能利用这些解释来建议修正后的译文。
英文摘要：
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these errors can potentially help improve the translation quality and user experience. This paper introduces xTower, an open large language model (LLM) built on top of TowerBase designed to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. The quality of the generated explanations by xTower are assessed via both intrinsic and extrinsic evaluation. We ask expert translators to evaluate the quality of the explanations across two dimensions: relatedness towards the error span being explained and helpfulness in error understanding and improving translation quality. Extrinsically, we test xTower across various experimental setups in generating translation corrections, demonstrating significant improvements in translation quality. Our findings highlight xTower's potential towards not only producing plausible and helpful explanations of automatic translations, but also leveraging them to suggest corrected translations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
