Summary of Meel: Multi-modal Event Evolution Learning, by Zhengwei Tao et al.
MEEL: Multi-Modal Event Evolution Learning
by Zhengwei Tao, Zhi Jin, Junqiang Huang, Xiancai Chen, Xiaoying Bai, Haiyan Zhao, Yifan Zhang, Chongyang Tao
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed Multi-Modal Event Evolution Learning (MEEL) approach aims to enhance machines’ ability to comprehend intricate event relations across diverse data modalities. By introducing a novel instruction encapsulation process and guiding discrimination strategy, MEEL enables models to grasp the underlying principles governing event evolution in various scenarios. The paper designates a benchmark, M-EV2, for MMER evaluation and demonstrates competitive performance on open-source multi-modal large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to teach machines to understand complex events across different types of data. Despite previous attempts to improve this ability, current AI models still struggle. To fix this, the study introduces a new approach called MEEL. It involves designing special instructions and using ChatGPT to generate evolving graphs. This helps AI models learn how to reason about events in a way that’s similar to humans. The researchers also create a benchmark dataset to test their method and show it works well with open-source AI language models. |
Keywords
» Artificial intelligence » Multi modal
