Summary of Ct2c-qa: Multimodal Question Answering Over Chinese Text, Table and Chart, by Bowen Zhao et al.
CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
by Bowen Zhao, Tianhao Cheng, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 C^2C-QA dataset aims to advance Multimodal Question Answering (MMQA) capabilities by integrating insights from diverse data representations such as text, tables, and charts. The dataset includes a comprehensive collection of multimodal data compiled from 200 selectively sourced webpages, simulating real-world scenarios where answers can appear in various modalities or not exist at all. The authors also introduce AED, a multi-agent system that leverages expert agents proficient in different modalities to deliver collective decision-making and decision-making. Experimental results demonstrate the limitations of current state-of-the-art models like GPT-4 compared to benchmarks set by C^2C-QA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new dataset for understanding questions that involve multiple types of data, such as text, tables, and charts. This type of question answering is important because it helps us understand things more comprehensively. The authors created a big collection of examples from the internet to test how well computer models can do this task. They also developed a way for these models to work together to make better decisions. |
Keywords
» Artificial intelligence » Gpt » Question answering
