Summary of Key-point-driven Data Synthesis with Its Enhancement on Mathematical Reasoning, by Yiming Huang et al.
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning
by Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, Weizhu Chen
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel data synthesis framework called Key-Point-Driven Data Synthesis (KPDDS) that generates question-answer pairs for complex reasoning tasks. The authors create the KPMath dataset, which contains over 800K question-answer pairs tailored for mathematical reasoning, and fine-tune a language model on this dataset to achieve state-of-the-art results on various math reasoning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special way to make fake questions and answers that are very good at helping computers learn. They made a big collection of these questions and answers called KPMath, which is super helpful for teaching computers how to solve math problems. They then used this dataset to train a computer program that can answer math questions really well. |
Keywords
» Artificial intelligence » Language model
