Summary of Chartinsights: Evaluating Multimodal Large Language Models For Low-level Chart Question Answering, by Yifan Wu et al.
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
by Yifan Wu, Lutao Yan, Leixian Shen, Yunhai Wang, Nan Tang, Yuyu Luo
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper focuses on improving the performance of multimodal large language models (MLLMs) in low-level ChartQA tasks, such as identifying correlations in visualization charts. To achieve this, the authors evaluate 19 advanced MLLMs, including GPT-4o, on a newly curated dataset called ChartInsights, which consists of 22,347 chart-task-query-answer pairs covering 10 data analysis tasks across 7 chart types. The results show that the average accuracy rate is 39.8%, with GPT-4o achieving the highest accuracy at 69.17%. To better understand the limitations of MLLMs in low-level ChartQA, the authors conduct experiments that alter visual elements of charts, such as changing color schemes or adding image noise. They also propose a new textual prompt strategy called Chain-of-Charts, which boosts performance by 14.41%, achieving an accuracy of 83.58%. Furthermore, incorporating a visual prompt strategy that directs attention to relevant visual elements further improves accuracy to 84.32%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding charts and graphs. It’s like when you try to figure out what a chart is showing by reading the words and looking at the picture. The researchers tested many different computer programs, including one called GPT-4o, on a big dataset of charts and questions. They found that these programs are pretty good at answering simple questions about charts, but they struggle with more complex tasks. To make them better, the researchers came up with some new ways to ask questions and show attention-grabbing pictures. This made the computers even better at understanding charts! |
Keywords
» Artificial intelligence » Attention » Gpt » Prompt
