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 fine-tuned DeepSeekMath model on KPMath-Plus achieves zero-shot PASS@1 accuracies of 83.9% on GSM8K and 48.8% on MATH, and also reaches promising performance on other math reasoning datasets, outperforming competitors in the 7B to 70B range.
PDF Abstract
GSM8K
MATH
SVAMP
