DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning

• URL: http://arxiv.org/abs/2407.04078v3
• Date: Wed, 17 Jul 2024 13:13:05 GMT
• Title: DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning
• Authors: Chengpeng Li, Guanting Dong, Mingfeng Xue, Ru Peng, Xiang Wang, Dayiheng Liu,
• Abstract summary: We introduce a series of large language models (LLMs) that employ 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, and engaging in self-reflection and correction. 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.
• Score: 24.68321102981711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On [55.449818944278526]
  We introduce the Skywork-Math model series, supervised fine-tuned (SFT) on common 7B language models. Skywork-Math 7B has achieved impressive accuracies of 51.2% on the competition-level MATH benchmark. We provide several practical takeaways to enhance math reasoning abilities in LLMs for both research and industry applications.
  arXiv  Detail & Related papers  (2024-07-11T09:56:51Z)
• MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning [11.426127461122908]
  This work includes new math questions via multi-perspective data augmenting methods and then synthesize code-nested solutions to them. Open Large Language Models (LLMs) that integrate with external Python interpreters have significantly enhanced mathematical reasoning capabilities. We propose a two-stage training strategy: In Stage-1, we finetune Llama-2 on pure CoT data to get an intermediate model, which then is trained on the code-nested data in Stage-2 to get the resulting MuMath-Code.
  arXiv  Detail & Related papers  (2024-05-13T08:32:19Z)
• MathScale: Scaling Instruction Tuning for Mathematical Reasoning [70.89605383298331]
  Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data.
  arXiv  Detail & Related papers  (2024-03-05T11:42:59Z)
• GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers [68.77382332826167]
  Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. One essential and frequently occurring evidence is that when the math questions are slightly changed, LLMs can behave incorrectly. This motivates us to evaluate the robustness of LLMs' math reasoning capability by testing a wide range of question variations.
  arXiv  Detail & Related papers  (2024-02-29T15:26:14Z)
• MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline [12.186691561822256]
  We postulate that the inherent nature of large language models (LLMs) presents challenges in modeling mathematical reasoning. This paper introduces a novel math dataset, enhanced with a capability to utilize a Python code interpreter. We propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs.
  arXiv  Detail & Related papers  (2024-01-16T08:08:01Z)
• MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning [54.2093509928664]
  In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective. We conduct an investigation for such data augmentation in math reasoning and are intended to answer these questions. We release our codes and augmented data in https://github.com/OFA-Sys/8k-Scel.
  arXiv  Detail & Related papers  (2023-10-09T08:18:58Z)
• MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning [52.97768001837269]
  We present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations. We propose a method of generating novel and high-quality datasets with math problems and their code-based solutions. This approach yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems.
  arXiv  Detail & Related papers  (2023-10-05T17:52:09Z)
• MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models [91.66694225955872]
  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. We release all the MetaMathQA dataset, the MetaMath models with different model sizes and the training code for public use.
  arXiv  Detail & Related papers  (2023-09-21T17:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.