@inproceedings{mazumder-etal-2026-multilingual,
title = "Multilingual Reasoning Cascades Need More Context",
author = "Mazumder, Arnav and
Zhang, Dengjia and
Li, Shuyue Stella and
Tsvetkov, Yulia and
Bafna, Niyati",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.24/",
pages = "412--461",
ISBN = "979-8-89176-474-3",
abstract = "Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation. We examine the benefits of a simple and training-free intervention: a context-aware translation cascade, which additionally provides the original question, the English-translated question, and the reasoning trace to the final cascade stage for target-language generation. We evaluate gains across ten multilingual benchmarks including various task types, three backbone models, and 285 high-, mid-, and low-resource languages, and demonstrate strong gains for open-ended generation across models and resource regimes. We show that the original language question carries most of the beneficial context. Our study emphasizes the need to better design information flow in multilingual reasoning cascades for mitigating error propagation, and provides a simple and actionable default strategy: preserve the original user question until the end of the pipeline."
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<abstract>Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation. We examine the benefits of a simple and training-free intervention: a context-aware translation cascade, which additionally provides the original question, the English-translated question, and the reasoning trace to the final cascade stage for target-language generation. We evaluate gains across ten multilingual benchmarks including various task types, three backbone models, and 285 high-, mid-, and low-resource languages, and demonstrate strong gains for open-ended generation across models and resource regimes. We show that the original language question carries most of the beneficial context. Our study emphasizes the need to better design information flow in multilingual reasoning cascades for mitigating error propagation, and provides a simple and actionable default strategy: preserve the original user question until the end of the pipeline.</abstract>
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%0 Conference Proceedings
%T Multilingual Reasoning Cascades Need More Context
%A Mazumder, Arnav
%A Zhang, Dengjia
%A Li, Shuyue Stella
%A Tsvetkov, Yulia
%A Bafna, Niyati
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F mazumder-etal-2026-multilingual
%X Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation. We examine the benefits of a simple and training-free intervention: a context-aware translation cascade, which additionally provides the original question, the English-translated question, and the reasoning trace to the final cascade stage for target-language generation. We evaluate gains across ten multilingual benchmarks including various task types, three backbone models, and 285 high-, mid-, and low-resource languages, and demonstrate strong gains for open-ended generation across models and resource regimes. We show that the original language question carries most of the beneficial context. Our study emphasizes the need to better design information flow in multilingual reasoning cascades for mitigating error propagation, and provides a simple and actionable default strategy: preserve the original user question until the end of the pipeline.
%U https://aclanthology.org/2026.wmt-main.24/
%P 412-461
Markdown (Informal)
[Multilingual Reasoning Cascades Need More Context](https://aclanthology.org/2026.wmt-main.24/) (Mazumder et al., WMT 2026)
ACL
- Arnav Mazumder, Dengjia Zhang, Shuyue Stella Li, Yulia Tsvetkov, and Niyati Bafna. 2026. Multilingual Reasoning Cascades Need More Context. In Proceedings of the Eleventh Conference on Machine Translation, pages 412–461, Budapest, Hungary. Association for Computational Linguistics.