Summary of Cllmate: a Multimodal Benchmark For Weather and Climate Events Forecasting, by Haobo Li et al.
CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting
by Haobo Li, Zhaowei Wang, Jiachen Wang, Yueya Wang, Alexis Kai Hon Lau, Huamin Qu
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This research proposes a new task called Weather and Climate Event Forecasting (WCEF) to predict weather and climate events through textual narratives, filling a gap in current environmental forecasting. The authors introduce CLLMate, a multimodal dataset for WCEF, comprising 26,156 news articles aligned with ERA5 reanalysis data. To evaluate the effectiveness of existing models, the researchers benchmarked 23 MLLMs on CLLMate, highlighting advantages and limitations. This work paves the way for developing more accurate WCEF models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting weather and climate events to help us prepare for natural disasters. Right now, scientists are great at predicting numbers like temperature, but they don’t do a good job of explaining what these numbers mean in simple language. The researchers propose a new task called Weather and Climate Event Forecasting (WCEF) that tries to fix this problem by using both numbers and words together. They create a big dataset called CLLMate with 26,000 articles about the weather, aligned with actual data from the past. Then, they test many different models on this dataset to see which ones work best. |
Keywords
* Artificial intelligence * Temperature
