# MuMath: Multi-perspective Data Augmentation for Mathematical Reasoning in Large Language Models ## Introduction We have amalgamated and further refined these strengths while broadening the scope of augmentation methods to construct a multi-perspective augmentation dataset for mathematics—termed [MuMath (μ-Math) Dataset](https://huggingface.co/datasets/weihao1/MuMath). Subsequently, we finetune LLaMA-2 on the MuMath dataset to derive the MuMath model. | Model | Size | GSM8k | MATH | |---|---|---|---| | WizardMath-7B | 7B | 54.9 | 10.7 | | MetaMath-7B | 7B | 66.3 | 19.7 | | MuggleMath-7B | 7B | 68.4 | - | | [MuMath-7B](https://huggingface.co/weihao1/MuMath-7B) | 7B | **79.1** | **30.0** | || | WizardMath-13B | 13B | 63.9 | 14 | | MetaMath-13B | 13B | 72.3 | 22.4 | | MuggleMath-13B | 13B | 74 | - |s | [MuMath-13B](https://huggingface.co/weihao1/MuMath-13B) | 13B | **83.6** | **33.3** | || | WizardMath-70B | 70B | 81.6 | 22.7 | | MetaMath-70B | 70B | 82.3 | 26.6 | | MuggleMath-70B | 70B | 82.3 | - | | [MuMath-70B](https://huggingface.co/weihao1/MuMath-70B) | 70B | **88.5** | **41.2** | > The best results are bolded. ## Augmentation Methods
Overview of the augmentation methods our MuMath employs, which can be divided into four categories: (1) Data Reformulation includes solution reorganization and question rephrasing; (2) Backward Creation includes Backward-Forward Transformation (BF-Trans) and FOBAR; (3) Question Alteration includes expression replacement and difficulty enhancement; (4) Nested Multi-task construction includes data of the auxiliary tasks, i.e., Problem Outline and Solution Plan.