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EMNLP Findings2024

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations

Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dongmei Zhang, Jia Li

Abstract

Existing research predominantly focuses on developing powerful large language models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we propose different training strategies to build powerful xMR LLMs, named MathOctopus, which notably outperform conventional open-source LLMs and exhibit superiority over ChatGPT in few-shot scenarios. Notably, MathOctopus-13B reaches 47.6% accuracy which exceeds ChatGPT 46.3% on MGSM testset. Beyond remarkable results, we unearth several pivotal observations and insights: (1) When extending the rejection sampling strategy to the multilingual context, it proves effective for model performances, albeit limited. (2) Employing parallel corpora for math Supervised Fine-Tuning (SFT) across multiple languages not only significantly enhances model performance multilingually and elevates their monolingual performance. This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks. For instance, MathOctopus-7B improves its counterparts that trained on English from 42.4% to 50.8% on the GSM8K test set. James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? English 詹姆斯决定每周跑 3 次 3 段冲刺,每段冲刺跑 60 米。他 每周一共跑多少米? Chinese James beschließt, 3-mal pro Woche 3 Kurzstreckenläufe zu absolvieren. Pro Kurzstreckenlauf läuft er 60 Meter. Wie viele Meter insgesamt läuft er pro Woche? German French Japanese …… He sprints 33=<<33=9>>9 time. So he runs 960=<<960 =540>>540 meters. English Chinese German French Japanese …… MathOctopus Question Answer 詹姆斯一共冲刺 33=<<33=9>>9 次。 所以他每周一共跑 960=<<960=540>>540米。 James sprintet 33=<<33=9>>9 Mal. Also läuft er 960= <<960=540>>540 Meter.

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Cited by top-tier papers33

  1. XCOT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought ReasoningLinzheng Chai, Jian Yang, Tao Sun, Hongcheng Guo et al.AAAI 2025 · 70 citations
  2. Linguistic Generalizability of Test-Time Scaling in Mathematical ReasoningGuijin Son, Jiwoo Hong, Hyunwoo Ko, James ThorneACL 2025 · 36 citations
  3. Multilingual Routing in Mixture-of-ExpertsLucas Bandarkar, Chenyuan Yang, Mohsen Fayyaz, Junlin Hu et al.ICLR 2026 · 34 citations
  4. MindMerger: Efficiently Boosting LLM Reasoning in non-English LanguagesZixian Huang, Wenhao Zhu, Gong Cheng, Lei Li et al.NeurIPS 2024 · 31 citations
  5. SimpleStrat: Diversifying Language Model Generation with StratificationJustin Wong, Yury Orlovskiy, Alexander Shypula, Michael Luo et al.NeurIPS 2025 · 17 citations

Builds on24

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
  2. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma et al.NeurIPS 2022 · 22,562 citations
  3. LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
  4. Multitask Prompted Training Enables Zero-Shot Task GeneralizationVictor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach et al.ICLR 2022 · 1,976 citations
  5. STaR: Bootstrapping Reasoning With ReasoningEric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. GoodmanNeurIPS 2022 · 1,126 citations

Related papers

  1. mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language ModelsHuiyuan Lai, Malvina NissimACL 2024 · 5 citations
  2. MAmmoTH: Building Math Generalist Models through Hybrid Instruction TuningXiang Yue, Xingwei Qu, Ge Zhang, Yao Fu et al.ICLR 2024 · 558 citations
  3. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructHaipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao et al.ICLR 2025
  4. MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMsZimu Lu, Aojun Zhou, Houxing Ren, Ke Wang et al.ACL 2024 · 11 citations
  5. MMATH: A Multilingual Benchmark for Mathematical ReasoningWenyang Luo, Wayne Xin Zhao, Jing Sha, Shijin Wang et al.EMNLP Findings 2025
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