@inproceedings{saji-etal-2026-reasoning,
title = "The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual {AI}",
author = "Saji, Alan and
Dabre, Raj and
Kunchukuttan, Anoop and
Puduppully, Ratish",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-short.25/",
pages = "329--344",
ISBN = "979-8-89176-381-4",
abstract = "Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nuances. We systematically compare an LRM{'}s reasoning in English versus the language of the question. Our evaluation spans two tasks: MGSM and GPQA Diamond. Beyond measuring answer accuracy, we also analyze cognitive attributes in the reasoning traces. We find that English reasoning traces exhibit a substantially higher presence of these cognitive behaviors, and that reasoning in English generally yields higher final-answer accuracy, with the performance gap increasing as tasks become more complex. However, this English-centric strategy is susceptible to a key failure mode - getting ``Lost in Translation,'' where translation steps lead to errors that would have been avoided by reasoning in the language of the question."
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<abstract>Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nuances. We systematically compare an LRM’s reasoning in English versus the language of the question. Our evaluation spans two tasks: MGSM and GPQA Diamond. Beyond measuring answer accuracy, we also analyze cognitive attributes in the reasoning traces. We find that English reasoning traces exhibit a substantially higher presence of these cognitive behaviors, and that reasoning in English generally yields higher final-answer accuracy, with the performance gap increasing as tasks become more complex. However, this English-centric strategy is susceptible to a key failure mode - getting “Lost in Translation,” where translation steps lead to errors that would have been avoided by reasoning in the language of the question.</abstract>
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%0 Conference Proceedings
%T The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI
%A Saji, Alan
%A Dabre, Raj
%A Kunchukuttan, Anoop
%A Puduppully, Ratish
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F saji-etal-2026-reasoning
%X Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nuances. We systematically compare an LRM’s reasoning in English versus the language of the question. Our evaluation spans two tasks: MGSM and GPQA Diamond. Beyond measuring answer accuracy, we also analyze cognitive attributes in the reasoning traces. We find that English reasoning traces exhibit a substantially higher presence of these cognitive behaviors, and that reasoning in English generally yields higher final-answer accuracy, with the performance gap increasing as tasks become more complex. However, this English-centric strategy is susceptible to a key failure mode - getting “Lost in Translation,” where translation steps lead to errors that would have been avoided by reasoning in the language of the question.
%U https://aclanthology.org/2026.eacl-short.25/
%P 329-344
Markdown (Informal)
[The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI](https://aclanthology.org/2026.eacl-short.25/) (Saji et al., EACL 2026)
ACL
- Alan Saji, Raj Dabre, Anoop Kunchukuttan, and Ratish Puduppully. 2026. The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 329–344, Rabat, Morocco. Association for Computational Linguistics.