Summary of Step Guided Reasoning: Improving Mathematical Reasoning Using Guidance Generation and Step Reasoning, by Lang Cao et al.
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning
by Lang Cao, Chao Peng, Renhong Chen, Wu Ning, Yingtian Zou, Yitong Li
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 proposed Step Guidied Reasoning method improves mathematical reasoning capabilities in large language models (LLMs) without relying on extensive inference datasets or few-shot methods that compromise accuracy. By incorporating a reflective process into the inference stage, LLMs can guide their reasoning through small steps, similar to human deliberation. This approach enables state-of-the-art language models like Qwen2-72B-Instruct to significantly outperform math-specific models on benchmarks like MMLU-STEM, achieving scores of 90.9% compared to 87.3%. The method also boosts average scores from 27.1% to 36.3% and from 36.5% to 47.4% in the mathematics domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Step Guidied Reasoning is a new way for large language models to learn math problems. It’s like how humans think, taking small steps to solve a problem. This helps the model do better on math tests without needing lots of practice data or quick shortcuts that might not be accurate. The method is tested and shown to work well, with one model called Qwen2-72B-Instruct getting high scores on math problems. |
Keywords
» Artificial intelligence » Few shot » Inference
