Summary of The Qiyas Benchmark: Measuring Chatgpt Mathematical and Language Understanding in Arabic, by Shahad Al-khalifa and Hend Al-khalifa
The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic
by Shahad Al-Khalifa, Hend Al-Khalifa
First submitted to arxiv on: 28 Jun 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 The paper introduces two novel benchmarks to evaluate language model performance in Arabic, specifically focusing on mathematical reasoning and language understanding abilities. The benchmarks are derived from the General Aptitude Test (Qiyas exam) used in Saudi Arabia for university admissions. The authors validate their benchmarks by assessing the performance of ChatGPT-3.5-trubo and ChatGPT-4 models. The results show that the benchmarks pose a significant challenge, with ChatGPT-4 achieving an overall average accuracy of 64% and ChatGPT-3.5-trubo achieving an overall accuracy of 49%. The release of these benchmarks is expected to enhance the capabilities of future language models tailored for low-resource Arabic language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates new tests to help machines understand Arabic math problems and texts better. They take a real test used in Saudi Arabia and use it to see how well some language models do on this kind of task. The results show that these tests are hard, but the best model got 64% correct and the other one got 49%. This new way of testing will help make machines smarter at understanding Arabic. |
Keywords
» Artificial intelligence » Language model » Language understanding
