Summary of Exposing the Achilles’ Heel: Evaluating Llms Ability to Handle Mistakes in Mathematical Reasoning, by Joykirat Singh et al.
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
by Joykirat Singh, Akshay Nambi, Vibhav Vineet
First submitted to arxiv on: 16 Jun 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 Large Language Models (LLMs) have been applied to Math Word Problems (MWPs) with significant impacts, revolutionizing problem-solving approaches. However, evaluations often prioritize final accuracy, neglecting reasoning capabilities. This paper addresses this gap by focusing on LLMs’ ability to detect and correct reasoning mistakes. The authors introduce a novel dataset, MWP-MISTAKE, containing MWPs with both correct and incorrect reasoning steps. Benchmarking reveals insights into the strengths and weaknesses of state-of-the-art models, such as GPT-4o, GPT-4, GPT-3.5Turbo, and others. The study highlights GPT-$o’s superior performance in mistake detection and rectification, while smaller models face persistent challenges. Additionally, issues related to data contamination and memorization impact LLM reliability in real-world applications. The findings emphasize the importance of rigorous evaluation of reasoning processes and propose future directions to enhance generalization and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models can solve math problems that require step-by-step thinking. Right now, these models are great at getting the right answer, but they don’t always understand why they got it right or wrong. The researchers created a special dataset with math word problems that have both correct and incorrect steps. They tested many different models to see how well they could detect mistakes and fix them. The results show that some models are much better than others at solving these problems correctly. The study highlights the importance of understanding why we get answers right or wrong, and proposes ways to improve these models for real-world use. |
Keywords
* Artificial intelligence * Generalization * Gpt
