Summary of Key-point-driven Mathematical Reasoning Distillation Of Large Language Model, by Xunyu Zhu et al.
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
by Xunyu Zhu, Jian Li, Can Ma, Weiping Wang
First submitted to arxiv on: 14 Jul 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 paper proposes a method called Key-Point-Driven Mathematical Reasoning Distillation (KPDD) to improve the mathematical reasoning abilities of Smaller Language Models (SLMs). By breaking down the problem-solving process into three stages, KPDD enhances the performance of SLMs. The approach is divided into two sub-methods: KPDD-CoT, which generates Chain-of-Thought rationales, and KPDD-PoT, which creates Program-of-Thought rationales. Experimental results show that KPDD-CoT significantly improves reasoning abilities, while KPDD-PoT achieves state-of-the-art performance in mathematical reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KPDD helps Smaller Language Models do math problems better by breaking the process into smaller steps. It makes two types of models: Chain-of-Thought and Program-of-Thought. The paper shows that this way of distilling large language models’ math abilities works well, making it easier to use these smaller models for tasks that require mathematical reasoning. |
Keywords
» Artificial intelligence » Distillation
