Summary of Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology, by Wei Xie et al.
Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology
by Wei Xie, Shuoyoucheng Ma, Zhenhua Wang, Enze Wang, Kai Chen, Xiaobing Sun, Baosheng Wang
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 Researchers investigated how Large Language Models (LLMs) solve mathematical problems, aiming to determine if they possess human-like mathematical reasoning. To address this question, the authors modified the Cognitive Reflection Test (CRT) problems and found that mainstream LLMs, including the o1 model, have a high error rate when solving these problems. The average accuracy rate dropped by up to 50% compared to the original CRT problems. Analysis of incorrect answers suggests that LLMs primarily rely on pattern matching from their training data, aligning with human intuition (System 1 thinking) rather than human-like reasoning (System 2 thinking). This finding challenges the idea that LLMs have genuine mathematical reasoning abilities comparable to humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how big language models do math problems. They want to know if these models are as good at math as people are. To test this, they changed some math problems and found out that the models made a lot of mistakes. The models were mostly guessing based on what they learned from their training data, rather than actually understanding the math like humans do. |
Keywords
» Artificial intelligence » Pattern matching
