通过迭代双语理解提升大模型翻译质量，减少误解导致的错误。
LLM-based Translation Inference with Iterative Bilingual Understanding
- 利用大模型跨语言能力分别理解源语言和目标语言上下文。
- 通过双语反馈循环迭代优化理解，多领域测试显著优于基线方法。
- 适合需要高精度、跨领域翻译的应用场景，如新闻与文化内容转换。
大语言模型（LLMs）强大的理解与生成能力显著提升了翻译性能。然而，对待翻译句子的错误理解会降低翻译质量。为此，我们提出一种基于 LLM 跨语言能力与翻译任务双重特性的新型迭代双语理解翻译方法（IBUT）。LLM 的跨语言能力可分别生成源语言和目标语言的上下文理解；同时，翻译任务的双重特性使 IBUT 能生成有效的跨语言反馈，迭代优化上下文理解，从而减少错误并提升翻译表现。实验结果表明，所提方法在多个领域（如新闻、常识推理和文化翻译基准）均优于多种强基线方法，具备良好泛化能力。
原文摘要 · Abstract (English)
The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance. However, incorrect understanding of the sentence to be translated can degrade translation quality. To address this issue, we proposed a novel Iterative Bilingual Understanding Translation (IBUT) method based on the cross-lingual capabilities of LLMs and the dual characteristics of translation tasks. The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately. Furthermore, the dual characteristics allow IBUT to generate effective cross-lingual feedback, iteratively refining contextual understanding, thereby reducing errors and improving translation performance. Experimental results showed that the proposed IBUT outperforms several strong comparison methods, especially being generalized to multiple domains (e.g., news, commonsense, and cultural translation benchmarks).
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