Summary of Brain-inspired Two-stage Approach: Enhancing Mathematical Reasoning by Imitating Human Thought Processes, By Yezeng Chen et al.
Brain-Inspired Two-Stage Approach: Enhancing Mathematical Reasoning by Imitating Human Thought Processes
by Yezeng Chen, Zui Chen, Yi Zhou
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper proposes a novel approach, called Brain, to improve large language models’ ability to perform complex mathematical reasoning tasks. The authors employ the Frontal Lobe Model to generate plans and the Parietal Lobe Model to generate code and execute to obtain answers. This method achieves state-of-the-art (SOTA) performance compared to Code LLaMA 7B based models. Additionally, the study finds that plans can be explicitly extracted from natural language, code, or formal language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps computers solve complex math problems better. The authors create a new way called Brain that makes computers think more like humans do when solving math problems. They use two different “models” to come up with plans and write code to get the answer. This approach works really well and is even better than other methods used before. |
Keywords
* Artificial intelligence * Llama
