Summary of Unleashing Reasoning Capability Of Llms Via Scalable Question Synthesis From Scratch, by Yuyang Ding et al.
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
by Yuyang Ding, Xinyu Shi, Xiaobo Liang, Juntao Li, Qiaoming Zhu, Min Zhang
First submitted to arxiv on: 24 Oct 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 The abstract presents a novel approach to creating high-quality data for Large Language Models (LLMs) that improves their reasoning capabilities. The existing methods have demonstrated the effectiveness of generating more instruction data from seed questions or knowledge bases, while recent research has shown that scaling up data synthesis from strong models like GPT-4 can further enhance reasoning performance. However, there is still a lack of high-quality data at scale and affordable costs in the open-sourced community. To address this issue, the authors introduce ScaleQuest, a scalable and novel data synthesis method that generates questions from scratch without requiring seed data or complex augmentation constraints. The method efficiently constructs a mathematical reasoning dataset with 1 million problem-solution pairs, which outperforms existing open-sourced datasets. This increased performance can be achieved by fine-tuning mainstream open-source models like Mistral, Llama3, DeepSeekMath, and Qwen2-Math, or even surpass proprietary models like GPT-4-Turbo and Claude-3.5 Sonnet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make Large Language Models better at reasoning. It’s about creating more data for these models so they can get smarter. Right now, there isn’t enough high-quality data available for free, which is a problem. The authors created a method called ScaleQuest that makes it easier and cheaper to generate lots of data without needing special seeds or complicated rules. They used this method to make a big dataset with millions of math problems and solutions, which is better than what’s already out there. This new data can help make existing models even stronger. |
Keywords
» Artificial intelligence » Claude » Fine tuning » Gpt
