Summary of Weatherqa: Can Multimodal Language Models Reason About Severe Weather?, by Chengqian Ma et al.
WeatherQA: Can Multimodal Language Models Reason about Severe Weather?
by Chengqian Ma, Zhanxiang Hua, Alexandra Anderson-Frey, Vikram Iyer, Xin Liu, Lianhui Qin
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 Medium Difficulty Summary: This paper introduces WeatherQA, a multimodal dataset designed to evaluate large foundation models’ ability to predict severe weather events. The dataset includes over 8,000 pairs of images and text describing environmental instability, surface observations, and radar reflectivity. These pairs are crucial for forecasting complex combinations of weather parameters (ingredients) that contribute to severe weather threats. The authors test state-of-the-art vision language models on two tasks: multi-choice QA for predicting affected areas and classification of development potential. They find a substantial gap between the strongest model, GPT4o, and human reasoning, suggesting that better training and data integration are necessary to bridge this gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research is about using computers to predict severe weather events like hail and tornadoes. Currently, computers can only predict simple changes in temperature or moisture. But what if they could understand complex combinations of weather conditions that lead to severe weather? The authors created a special dataset called WeatherQA with over 8,000 pairs of images and text that describe these weather conditions. They tested some of the best computer models on this task and found that there is still a big gap between what computers can do and what humans can do. |
Keywords
» Artificial intelligence » Classification » Temperature
