Summary of Scemqa: a Scientific College Entrance Level Multimodal Question Answering Benchmark, by Zhenwen Liang et al.
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark
by Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. The benchmark focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology, featuring both multiple-choice and free-response formats to comprehensively evaluate AI models’ abilities. The paper also highlights the unique aspects of SceMQA, such as presenting problems with identical contexts but varied questions to facilitate a more thorough assessment of reasoning capabilities. The experiment evaluates open-source and close-source state-of-the-art Multimodal Large Language Models (MLLMs) across various experimental settings, showing that further research is needed in developing more capable MLLMs. The results indicate that only 50% to 60% accuracy was achieved by the strongest models, emphasizing the need for continued development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test how well AI can answer science questions. This is important because many students are struggling with science and math in high school and college. The new benchmark, called SceMQA, tests AI’s ability to understand science and math concepts and answer questions correctly. It’s like a big test that makes sure the AI can really understand what it’s talking about. The experiment tested some of the best AI models out there, but they didn’t do very well. The results show that we still need to work on making these AI models better at understanding science and math. This is important because it will help us create better tools for learning and education. |
Keywords
» Artificial intelligence » Question answering
