Summary of First Multi-dimensional Evaluation Of Flowchart Comprehension For Multimodal Large Language Models, by Enming Zhang et al.
First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models
by Enming Zhang, Ruobing Yao, Huanyong Liu, Junhui Yu, Jiale Wang
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 FlowCE method evaluates Multimodal Large Language Models (MLLMs) on various dimensions for tasks related to flowcharts, covering Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization. The results show that even the GPT4o model achieves a score of only 56.63, while Phi-3-Vision obtained the highest open-source model score of 49.97. This research aims to contribute to future studies on MLLMs for flowchart-based tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a method called FlowCE to evaluate Multimodal Large Language Models (MLLMs) for tasks related to flowcharts. Flowcharts are important in daily life and work, but there wasn’t a good way to test how well MLLMs could do these tasks. The FlowCE method looks at different skills like reasoning, recognizing things on the chart, getting information from it, making sure the logic is correct, and summarizing what’s on the chart. Even the best model, GPT4o, didn’t do very well, scoring only 56.63 out of a possible score. Phi-3-Vision was the top-scoring open-source model with a score of 49.97. |
Keywords
» Artificial intelligence » Summarization
