MetaMath: Bootstrap Your Own Mathematical Questions for Large Language
  Models
Large language models (LLMs) have pushed the limits of natural language
understanding and exhibited excellent problem-solving ability. Despite the
great success, most existing open-source LLMs (\eg, LLaMA-2) are still far away
from satisfactory for solving mathematical problem due to the complex reasoning
procedures. To bridge this gap, we propose MetaMath, a fine-tuned
language model that specializes in mathematical reasoning. Specifically, we
start by bootstrapping mathematical questions by rewriting the question from
multiple perspectives without extra knowledge, which results in a new dataset
called {MetaMathQA}. Then we fine-tune the LLaMA-2 models on MetaMathQA.
Experimental results on two popular benchmarks (\ie, GSM8K and MATH) for
mathematical reasoning demonstrate that MetaMath outperforms a suite of
open-source LLMs by a significant margin. Our MetaMath-7B model achieves
66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models
of the same size by 11.5% and 8.7%. Particularly, {MetaMath-70B} achieves
an accuracy of 82.3% on {GSM8K}, slightly better than {GPT-3.5-Turbo}. We
release the {MetaMathQA} dataset, the {MetaMath} models with different model
sizes and the training code for public use.
650d11852fe3e08aa64fc16a
Longhui Yu
admin_assigned
2023-09-22T15:18:35.033Z
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Longhui Yu
user
Longhui98
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Weisen Jiang
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2023-09-22T17:53:40.335Z
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Weisen Jiang
user
waysonkong
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Han Shi
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2023-09-22T17:53:37.317Z
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Han Shi
user
shihan96
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Jincheng Yu
admin_assigned
2023-09-22T15:19:22.393Z
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YU Jincheng
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yujincheng08
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Zhengying Liu
admin_assigned
2023-09-22T15:19:31.044Z
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Zhengying Liu
user
evariste-liu
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Yu Zhang
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James T. Kwok
admin_assigned
2023-09-22T15:21:25.334Z
647015f2dcddb2358a287f2f
/avatars/01f7df1ad30d70c99ef1c3521bbeac94.svg
James Kwok
user
KazumaMyGuy
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Zhenguo Li
admin_assigned
2023-09-22T15:21:38.107Z
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lzg
user
lizhenguo
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Adrian Weller
650d11852fe3e08aa64fc173
Weiyang Liu
admin_assigned
2023-09-22T15:21:49.980Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/648905d1a15c43c791d4381f/GpqGBzsLiMHX0gWZEz3qn.jpeg
Weiyang Liu
user
wy1iu
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language
  Models
Large language models (LLMs) have pushed the limits of natural language
understanding and exhibited excellent problem-solving ability. Despite the
great success, most existing open-source LLMs (\eg, LLaMA-2) are still far away
from satisfactory for solving mathematical problem due to the complex reasoning
procedures. To bridge this gap, we propose MetaMath, a fine-tuned
language model that specializes in mathematical reasoning. Specifically, we
start by bootstrapping mathematical questions by rewriting the question from
multiple perspectives without extra knowledge, which results in a new dataset
called {MetaMathQA}. Then we fine-tune the LLaMA-2 models on MetaMathQA.
Experimental results on two popular benchmarks (\ie, GSM8K and MATH) for
mathematical reasoning demonstrate that MetaMath outperforms a suite of
open-source LLMs by a significant margin. Our MetaMath-7B model achieves
66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models
of the same size by 11.5% and 8.7%. Particularly, {MetaMath-70B} achieves
an accuracy of 82.3% on {GSM8K}, slightly better than {GPT-3.5-Turbo}. We
release the {MetaMathQA} dataset, the {MetaMath} models with different model
sizes and the training code for public use.
MetaMath, a fine-tuned language model specializing in mathematical reasoning, outperforms existing models on benchmarks like GSM8K and MATH through the use of a new dataset, MetaMathQA.
MetaMath LLaMA-2 MetaMathQA mathematical reasoning GSM8K MATH MetaMath-7B MetaMath-70B GPT-3.5-Turbo