Summary of Achieving >97% on Gsm8k: Deeply Understanding the Problems Makes Llms Better Solvers For Math Word Problems, by Qihuang Zhong et al.
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
by Qihuang Zhong, Kang Wang, Ziyang Xu, Juhua Liu, Liang Ding, Bo Du
First submitted to arxiv on: 23 Apr 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 This abstract presents a solution to improve Large Language Models’ (LLMs) performance in solving complex math word problems. Current Chain-of-Thought (CoT) prompting methods struggle with semantic misunderstanding errors, calculation errors, and step-missing errors. The proposed method, Deeply Understanding the Problems (DUP), addresses these issues by encouraging LLMs to deeply understand problems and extract key information for better reasoning. DUP outperforms existing methods on 10 diverse benchmarks, achieving a new state-of-the-art result on GSM8K with an accuracy of 97.1% under zero-shot setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models do math better! Right now, these models are really good at answering questions, but they struggle to solve complex math problems. That’s because they often misunderstand what the problem is saying, or forget important steps in their calculations. The authors of this paper came up with a new way to help LLMs understand math problems better. It’s called Deeply Understanding the Problems (DUP), and it works by encouraging the model to really think about what the problem is asking, and then use that information to make good decisions. This approach did really well on lots of different tests, and even set a new record for doing math problems correctly! |
Keywords
» Artificial intelligence » Prompting » Zero shot
