现代机器翻译新趋势：基于大型推理模型

New Trends for Modern Machine Translation with Large Reasoning Models

March 13, 2025

作者: Sinuo Liu, Chenyang Lyu, Minghao Wu, Longyue Wang, Weihua Luo, Kaifu Zhang

cs.AI

摘要

近期，大型推理模型（LRMs）的进展，尤其是那些利用思维链推理（CoT）的模型，为机器翻译（MT）开辟了全新的可能性。本立场文件主张，LRMs通过将翻译重构为一项需要上下文、文化和语言理解与推理的动态任务，从根本上改变了传统的神经机器翻译以及基于大语言模型（LLMs）的翻译范式。我们识别了三大基础性转变：1）上下文连贯性，LRMs通过显式推理跨越句子及复杂上下文，甚至在缺乏上下文的情况下，解决歧义并保持语篇结构；2）文化意图性，使模型能够通过推断说话者意图、受众期待及社会语言规范来调整输出；3）自我反思，LRMs在推理过程中进行自我反思，以纠正翻译中的潜在错误，特别是在极端嘈杂的情况下，展现出比简单的X->Y映射翻译更好的鲁棒性。我们通过展示实证案例，探讨了翻译中的多种场景，包括风格化翻译、文档级翻译和多模态翻译，证明了LRMs在翻译中的优越性。同时，我们也指出了LRMs在MT中的一些有趣现象，如自动枢纽翻译，以及面临的重大挑战，如翻译中的过度本地化和推理效率问题。总之，我们认为LRMs重新定义了翻译系统，使其不仅仅是文本转换器，而是能够推理文本之外意义的多语言认知代理。这一范式转变提醒我们，在更广泛的背景下，利用LRMs思考翻译问题——我们能在其上实现什么。

English

Recent advances in Large Reasoning Models (LRMs), particularly those leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility for Machine Translation (MT). This position paper argues that LRMs substantially transformed traditional neural MT as well as LLMs-based MT paradigms by reframing translation as a dynamic reasoning task that requires contextual, cultural, and linguistic understanding and reasoning. We identify three foundational shifts: 1) contextual coherence, where LRMs resolve ambiguities and preserve discourse structure through explicit reasoning over cross-sentence and complex context or even lack of context; 2) cultural intentionality, enabling models to adapt outputs by inferring speaker intent, audience expectations, and socio-linguistic norms; 3) self-reflection, LRMs can perform self-reflection during the inference time to correct the potential errors in translation especially extremely noisy cases, showing better robustness compared to simply mapping X->Y translation. We explore various scenarios in translation including stylized translation, document-level translation and multimodal translation by showcasing empirical examples that demonstrate the superiority of LRMs in translation. We also identify several interesting phenomenons for LRMs for MT including auto-pivot translation as well as the critical challenges such as over-localisation in translation and inference efficiency. In conclusion, we think that LRMs redefine translation systems not merely as text converters but as multilingual cognitive agents capable of reasoning about meaning beyond the text. This paradigm shift reminds us to think of problems in translation beyond traditional translation scenarios in a much broader context with LRMs - what we can achieve on top of it.