title: DotaMath: Decomposition of Thought with Code Assistance and
    Self-correction for Mathematical Reasoning
summary: 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.
paper_authors._id: 668b943035da6da695a9a392
paper_authors.name: Chengpeng Li
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paper_authors.user: ChengpengLi
paper_authors._id: 668b943035da6da695a9a393
paper_authors.name: Guanting Dong
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paper_authors.fullname: KABI
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paper_authors.user: dongguanting
paper_authors._id: 668b943035da6da695a9a394
paper_authors.name: Mingfeng Xue
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paper_authors.fullname: Mingfeng Xue
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paper_authors.user: mingfengxue
paper_authors._id: 668b943035da6da695a9a395
paper_authors.name: Ru Peng
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paper_authors.fullname: Ru Peng
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paper_authors.user: RuPeng
paper_authors._id: 668b943035da6da695a9a396
paper_authors.name: Xiang Wang
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paper_authors.fullname: Xiang Wang
paper_authors.type: user
paper_authors.user: xiangwang1223
paper_authors._id: 668b943035da6da695a9a397
paper_authors.name: Dayiheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-08T21:12:56.332Z
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paper_authors.fullname: Dayiheng Liu
paper_authors.type: user
paper_authors.user: Losin94
paper_title: DotaMath: Decomposition of Thought with Code Assistance and
    Self-correction for Mathematical Reasoning
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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