Summary of Measuring Vision-language Stem Skills Of Neural Models, by Jianhao Shen et al.
Measuring Vision-Language STEM Skills of Neural Models
by Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan, Ming Zhang, Chenguang Wang
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 This paper introduces a new challenge to test the STEM skills of neural models by requiring them to understand multimodal vision-language information in STEM. The dataset features 448 skills and 1,073,146 questions spanning all STEM subjects, with fundamental skills and questions designed based on the K-12 curriculum. Unlike existing datasets that focus on expert-level ability, this dataset includes questions for lower grade levels. State-of-the-art foundation models like CLIP and GPT-3.5-Turbo are benchmarked against the dataset, showing limited progress in mastering STEM skills. Despite training models on the dataset, performance remains relatively low compared to average elementary students. The paper highlights the need for novel algorithmic innovations from the community to solve real-world STEM problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new challenge to test how well computer models can understand science, technology, engineering, and math (STEM) concepts. Most datasets only ask if a model is good at answering expert-level questions. This one asks simpler questions based on what kids learn in school. The best current AI models don’t do very well on this challenge. Even when they’re trained on the same data, they still don’t perform as well as elementary students. To solve real-world STEM problems, we need to come up with new ideas for how to improve these models. |
Keywords
* Artificial intelligence * Gpt
