Summary of Evaluating Large Language Models on the Gmat: Implications For the Future Of Business Education, by Vahid Ashrafimoghari et al.
Evaluating Large Language Models on the GMAT: Implications for the Future of Business Education
by Vahid Ashrafimoghari, Necdet Gürkan, Jordan W. Suchow
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper introduces a benchmark to assess the performance of seven Large Language Models (LLMs) on the Graduate Management Admission Test (GMAT). It shows that most LLMs outperform human candidates, with GPT-4 Turbo surpassing average scores of graduate students at top business schools. The study also examines GPT-4 Turbo’s ability to explain answers, evaluate responses, and generate alternative scenarios. The latest LLM versions show marked improvements in reasoning tasks compared to their predecessors, underscoring their potential for complex problem-solving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well seven Large Language Models do on the GMAT exam. It finds that most of these models are better than human students. One model, GPT-4 Turbo, does even better and gets higher scores than graduate students from top business schools. The study also shows how well this model can explain answers, check responses for errors, and come up with new scenarios. As AI models get better at complex problem-solving, they could be helpful in education. |
Keywords
» Artificial intelligence » Gpt
