Summary of Kwai-star: Transform Llms Into State-transition Reasoners, by Xingyu Lu et al.
Kwai-STaR: Transform LLMs into State-Transition Reasoners
by Xingyu Lu, Yuhang Hu, Changyi Liu, Tianke Zhang, Zhenyu Yang, Zhixiang Ding, Shengsheng Qian, Meng Du, Ruiwen Kang, Kaiyu Tang, Fan Yang, Tingting Gao, Di Zhang, Hai-Tao Zheng, Bin Wen
First submitted to arxiv on: 7 Nov 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 discusses the challenges LLMs face in mathematical reasoning and proposes a novel framework called Kwai-STaR to enhance their intuitive reasoning capabilities. The framework transforms LLMs into State-Transition Reasoners by defining state space tailored to mathematical reasoning, generating state-transition data, and training them using a curricular strategy. Experiments validate the effectiveness of Kwai-STaR in improving mathematical reasoning on datasets like GSM8K and GSM-Hard, with notable performance gains for models like Mistral-7B and LLaMA-3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models better at solving math problems. Right now, these models struggle to reason mathematically, but researchers have come up with a new way to help them get better. It’s called Kwai-STaR, and it works by giving the models special training that helps them think more like humans when they’re doing math. The results are impressive – the models can solve math problems much better than before! This could be really important for things like education and science. |
Keywords
» Artificial intelligence » Llama
