Summary of Llms Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark For Comprehensive Evaluation Of Llms, by Arash Gholami Davoodi et al.
LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs
by Arash Gholami Davoodi, Seyed Pouyan Mousavi Davoudi, Pouya Pezeshkpour
First submitted to arxiv on: 7 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 The paper presents the Mathematical Topics Tree (MaTT) benchmark to evaluate large language models’ (LLMs’) mathematical reasoning capabilities. The MaTT benchmark comprises 1,958 questions across various mathematical subjects, each linked to a hierarchical chain of topics. Using this benchmark, the authors assess different LLMs, including GPT-4, which achieved only 54% accuracy in a multiple-choice scenario. Interestingly, Chain-of-Thought prompting did not significantly improve performance. Furthermore, when choices were not provided, LLM accuracy dropped by up to 24.2 percentage points. The study also analyzed the completeness and correctness of generated explanations and found that even when correct answers were provided, accompanying explanations were only complete and accurate in 53.3% of cases. This highlights the need for more comprehensive evaluations of LLMs’ mathematical reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how well artificial intelligence models do math problems. They created a big test with lots of different math questions to see how good these models are at solving them. The tests showed that even the best models didn’t do very well, especially when they didn’t have multiple-choice answers to choose from. When they did get answers correct, it turns out they often didn’t explain why they were right, which is important for genuine understanding. This study helps us understand how good these AI models are at math and what we need to improve them. |
Keywords
» Artificial intelligence » Gpt » Prompting
