Summary of From Blind Solvers to Logical Thinkers: Benchmarking Llms’ Logical Integrity on Faulty Mathematical Problems, by a M Muntasir Rahman et al.
From Blind Solvers to Logical Thinkers: Benchmarking LLMs’ Logical Integrity on Faulty Mathematical Problems
by A M Muntasir Rahman, Junyi Ye, Wei Yao, Wenpeng Yin, Guiling Wang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study sheds light on the limitations of large language models (LLMs) in solving math problems. Researchers found that many LLMs rely on simple arithmetic calculations rather than logical thinking to arrive at solutions. This is demonstrated by a classic math problem where Lily receives 3 cookies and eats 5, only to be given 3 more cookies, resulting in an answer of “1” when the correct solution requires acknowledging the initial cookie count as insufficient for eating 5. The study highlights whether LLMs are simply “Blind Solvers” or can truly function as “Logical Thinkers” capable of identifying and addressing logical inconsistencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Do you like math problems? A new study looks at how computers solve math puzzles. It seems that many computer programs, called large language models (LLMs), just do simple addition and subtraction to get the answer. But humans know better – we can see that Lily wouldn’t have enough cookies for breakfast if she only had 3 to start with! The study asks: are these LLMs really good at math or are they just doing simple calculations? |
