Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- code
+- math
 ---
+# MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs
+
+This is a model for the paper "[MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs](https://arxiv.org/pdf/2402.16352.pdf)".
+
+
+
+
+## News
+
+- **[2024-02-26]** Our paper is now accessible at [ArXiv Paper](https://arxiv.org/pdf/2402.16352.pdf).
+
+## Introduction
+
+Large language models (LLMs) have exhibited great potential in mathematical reasoning. However, there remains a performance gap in this area between existing open-source models and closed-source models such as GPT-4. 
+
+In this paper, we introduce **MathGenie**, a novel method for generating diverse and reliable math problems from a small-scale problem-solution dataset (denoted as *seed data*). We augment the ground-truth solutions of our seed data and train a back-translation model to translate the augmented solutions back into new questions. Subsequently, we generate code-integrated solutions for the new questions. To ensure the correctness of the code-integrated solutions, we employ rationale-based strategy for solution verification. 
+
+Various pretrained models, ranging from 7B to 70B, are trained on the newly curated data to test the effectiveness of the proposed augmentation technique, resulting in a family of models known as *MathGenieLM*. These models consistently outperform previous open-source models across five representative mathematical reasoning datasets, achieving state-of-the-art performance. In particular, MathGenieLM-InternLM2 achieves an accuracy of 87.7% on GSM8K and 55.7% on MATH, securing the best overall score among open-source language models.
+
+You can refer to the [project homepage](https://mathgenie.github.io/) and [the paper](https://arxiv.org/pdf/2402.16352.pdf) for more details.
+
+## Usage
+
+### Models
+
+Our [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) model is available at Huggingface now.
+Our [MathGenie-Mixtral-8x7B](https://huggingface.co/MathGenie/MathGenie-Mixtral-8x7B) model is available at Huggingface now.
+
+| Base Model   | Model                                                        |
+| ------------ | ------------------------------------------------------------ |
+| InternLM-20B | [MathGenie-InterLM-20B](https://huggingface.co/MathGenie/MathGenie-InterLM-20B) |
+| Mixtral-8x7B | [MathGenie-Mixtral-8x7B](https://huggingface.co/MathGenie/MathGenie-Mixtral-8x7B) |
+
+### Inference & Evaluation
+
+Please refer to the [MathCoder repo](https://github.com/mathllm/MathCoder) for the detailed code for inference and evaluation of our MathGenieLM models.
+
+## Citation
+
+If you find this paper helpful to your research, please kindly cite this BibTex:
+
+```
+@misc{lu2024mathgenie,
+			title={MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs}, 
+      author={Zimu Lu and Aojun Zhou and Houxing Ren and Ke Wang and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
+      year={2024},
+      eprint={2402.16352},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+```
+@inproceedings{
+			wang2024mathcoder,
+			title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
+			author={Ke Wang and Houxing Ren and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
+			booktitle={The Twelfth International Conference on Learning Representations},
+			year={2024},
+			url={https://openreview.net/forum?id=z8TW0ttBPp}
+}
+```