Feature Extraction
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  ### Introduction
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  We introduce πŸ™ MathOctopus, a series of open-source large language models (LLMs) specifically tailored for multilingual math problem-solving. The MathOctopus models are trained on πŸ€— MGSM8KInstruct Dataset, encompassing ten distinct languages.
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  | MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
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  ## Intended Uses
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- These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # πŸ™ Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
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+ Project Page: [https://mathoctopus.github.io/](https://mathoctopus.github.io/)
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+ Paper: [https://arxiv.org/abs/2310.20246.pdf](https://arxiv.org/abs/2310.20246.pdf)
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+ Code: [https://github.com/microsoft/MathOctopus](https://github.com/microsoft/MathOctopus)
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  ### Introduction
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  We introduce πŸ™ MathOctopus, a series of open-source large language models (LLMs) specifically tailored for multilingual math problem-solving. The MathOctopus models are trained on πŸ€— MGSM8KInstruct Dataset, encompassing ten distinct languages.
 
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  | MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
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  ## Intended Uses
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+ These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
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+ ## Citation
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+ Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers.
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+ ```
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+ @misc{chen2023breaking,
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+ title={Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations},
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+ author={Nuo Chen and Zinan Zheng and Ning Wu and Linjun Shou and Ming Gong and Yangqiu Song and Dongmei Zhang and Jia Li},
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+ year={2023},
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+ eprint={2310.20246},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```