Summary of Skywork-math: Data Scaling Laws For Mathematical Reasoning in Large Language Models — the Story Goes On, by Liang Zeng et al.
Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models – The Story Goes On
by Liang Zeng, Liangjun Zhong, Liang Zhao, Tianwen Wei, Liu Yang, Jujie He, Cheng Cheng, Rui Hu, Yang Liu, Shuicheng Yan, Han Fang, Yahui Zhou
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 The paper explores the factors that improve large language models’ (LLMs) mathematical reasoning capabilities. It argues that even modern LLMs have untapped potential, with performance increasing proportionally to data quantity. To test this hypothesis, the authors introduce the Skywork-Math model series, trained on a custom dataset and achieving impressive results on benchmarks like MATH (51.2%) and GSM8K (83.9%). The superior performance is attributed to novel data synthesis and training pipelines, including three augmentation methods and a diverse problem set. This research provides practical takeaways for enhancing math reasoning abilities in LLMs for both research and industry applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can get better at doing math problems. The idea is that even really good models have room to improve, as long as they’re given more data. To test this idea, the researchers created a new kind of model called Skywork-Math. This model did really well on tests and showed that it’s possible to make big language models better at doing math. |
