New Trends for Modern Machine Translation with Large Reasoning Models
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.
67d39b35acb72b994659d4fd
Sinuo Liu
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2025-03-14T10:46:07.508Z
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LiuSinuo
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SNF
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Chenyang Lyu
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2025-03-14T10:46:14.863Z
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Chenyang Lyu
user
ChenyangLyu
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Minghao Wu
admin_assigned
2025-03-14T10:46:28.934Z
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Minghao Wu
user
minghaowu
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Longyue Wang
admin_assigned
2025-03-14T10:46:48.590Z
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Longyue Wang
user
longyuewang
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Weihua Luo
admin_assigned
2025-03-14T10:46:55.026Z
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Weihua Luo
user
acecamel1977
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Kaifu Zhang
admin_assigned
2025-03-14T10:47:02.246Z
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Kaifu Zhang
user
zhangkaifu314
New Trends for Modern Machine Translation with Large Reasoning Models
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.
Large Reasoning Models leveraging Chain-of-Thought reasoning have redefined Machine Translation by transforming it into a dynamic reasoning task that emphasizes contextual coherence, cultural intent, and self-reflection, demonstrating significant improvements in various translation scenarios.
Large Reasoning Models Chain-of-Thought reasoning neural MT contextual coherence cultural intentionality self-reflection stylized translation document-level translation multimodal translation auto-pivot translation over-localisation inference efficiency multilingual cognitive agents