DotaMath: Decomposition of Thought with Code Assistance and
  Self-correction for Mathematical Reasoning
Large language models (LLMs) have made impressive progress in handling simple
math problems, yet they still struggle with more challenging and complex
mathematical tasks. In this paper, we introduce a series of LLMs that employs
the Decomposition of thought with code assistance and self-correction for
mathematical reasoning, dubbed as DotaMath. DotaMath models tackle complex
mathematical tasks by decomposing them into simpler logical subtasks,
leveraging code to solve these subtasks, obtaining fine-grained feedback from
the code interpreter, and engaging in self-reflection and correction. By
annotating diverse interactive tool-use trajectories and employing query
evolution on GSM8K and MATH datasets, we generate an instruction fine-tuning
dataset called DotaMathQA with 574K query-response pairs. We train a series of
base LLMs using imitation learning on DotaMathQA, resulting in DotaMath models
that achieve remarkable performance compared to open-source LLMs across various
in-domain and out-of-domain benchmarks. Notably, DotaMath-deepseek-7B showcases
an outstanding performance of 64.8% on the competitive MATH dataset and 86.7%
on GSM8K. Besides, DotaMath-deepseek-7B maintains strong competitiveness on a
series of in-domain and out-of-domain benchmarks (Avg. 80.1%). Looking forward,
we anticipate that the DotaMath paradigm will open new pathways for addressing
intricate mathematical problems. Our code is publicly available at
https://github.com/ChengpengLi1003/DotaMath.
668b943035da6da695a9a392
Chengpeng Li
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2024-07-08T21:12:23.011Z
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ChengpengLi
user
ChengpengLi
668b943035da6da695a9a393
Guanting Dong
admin_assigned
2024-07-08T21:12:36.856Z
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KABI
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dongguanting
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Mingfeng Xue
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2024-07-08T08:49:40.844Z
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Mingfeng Xue
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mingfengxue
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Ru Peng
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2024-07-08T21:12:46.126Z
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Ru Peng
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RuPeng
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Xiang Wang
admin_assigned
2024-07-08T21:13:54.374Z
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Xiang Wang
user
xiangwang1223
668b943035da6da695a9a397
Dayiheng Liu
admin_assigned
2024-07-08T21:12:56.332Z
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Dayiheng Liu
user
Losin94
DotaMath: Decomposition of Thought with Code Assistance and
  Self-correction for Mathematical Reasoning
Large language models (LLMs) have made impressive progress in handling simple
math problems, yet they still struggle with more challenging and complex
mathematical tasks. In this paper, we introduce a series of LLMs that employs
the Decomposition of thought with code assistance and self-correction for
mathematical reasoning, dubbed as DotaMath. DotaMath models tackle complex
mathematical tasks by decomposing them into simpler logical subtasks,
leveraging code to solve these subtasks, obtaining fine-grained feedback from
the code interpreter, and engaging in self-reflection and correction. By
annotating diverse interactive tool-use trajectories and employing query
evolution on GSM8K and MATH datasets, we generate an instruction fine-tuning
dataset called DotaMathQA with 574K query-response pairs. We train a series of
base LLMs using imitation learning on DotaMathQA, resulting in DotaMath models
that achieve remarkable performance compared to open-source LLMs across various
in-domain and out-of-domain benchmarks. Notably, DotaMath-deepseek-7B showcases
an outstanding performance of 64.8% on the competitive MATH dataset and 86.7%
on GSM8K. Besides, DotaMath-deepseek-7B maintains strong competitiveness on a
series of in-domain and out-of-domain benchmarks (Avg. 80.1%). Looking forward,
we anticipate that the DotaMath paradigm will open new pathways for addressing
intricate mathematical problems. Our code is publicly available at
https://github.com/ChengpengLi1003/DotaMath.
DotaMath, a series of LLMs using decomposition with code assistance and self-correction, achieves high performance on complex mathematical tasks by fine-tuning on a large dataset of interactive tool Use.
LLMs Decomposition of thought code assistance self-correction mathematical reasoning DotaMath logical subtasks code interpreter self-reflection annotation query evolution imitation learning DotaMathQA in-domain benchmarks out-of-domain benchmarks MATH dataset GSM8K DotaMath-deepseek-7B