Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning
<|reference_start|>Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning: Large language models (LLMs) have shown great potential in complex reasoning tasks, yet their performance is often hampered by the scarcity of high-quality and reasoning-focused training datasets. Addressing this challenge, we propose Key-Point-Driven Data Synthesis (KPDDS), a novel data synthesis framework that synthesizes question-answer pairs by leveraging key points and exemplar practices from authentic data sources. KPDDS ensures the generation of novel questions with rigorous quality control and substantial scalability. As a result, we present KPMath, an extensive synthetic dataset tailored for mathematical reasoning, comprising over 800K question-answer pairs. Utilizing KPMath and augmenting it with additional reasoning-intensive corpora, we create the comprehensive KPMath-Plus dataset. The Qwen1.5-72B model, fine-tuned on KPMath-Plus, achieves 87.0% PASS@1 accuracy on GSM8K and 58.3% on MATH, surpassing competitors in the 7B to 70B range and best commercial models like GPT-4 across multiple math reasoning datasets.<|reference_end|>
@article{huang2024key-point-driven,
    title={Key-Point-Driven Data Synthesis with its Enhancement on Mathematical
  Reasoning},
    author={Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan,
  Weizhu Chen},
    journal={arXiv preprint arXiv:2403.02333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02333},
    primaryClass={cs.CL cs.AI}
}