Summary of Chartmoe: Mixture Of Diversely Aligned Expert Connector For Chart Understanding, by Zhengzhuo Xu et al.
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
by Zhengzhuo Xu, Bowen Qu, Yiyan Qi, Sinan Du, Chengjin Xu, Chun Yuan, Jian Guo
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel architecture for automatic chart understanding, dubbed ChartMoE, which employs the Mixture of Expert (MoE) architecture to bridge the modality gap between charts and text data. The approach utilizes multiple linear connectors trained through distinct alignment tasks as foundational initialization parameters for different experts. This is achieved by introducing ChartMoE-Align, a dataset with nearly 1 million chart-table-JSON-code quadruples, used to conduct three alignment tasks (chart-table/JSON/code). The MoE connector and LLM parameters are then refined through high-quality knowledge learning. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 84.64% on the ChartQA benchmark, a significant improvement over the previous state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChartMoE is a new way for computers to understand charts and tables. This can help with things like reading documents and doing data analysis. The problem is that current computer models are not very good at this because they struggle to connect chart information to text information. ChartMoE uses a special type of model called MoE (Mixture of Experts) to fix this issue. It does this by training multiple smaller models, each one focused on a different aspect of the data, and then combining their results. This approach is tested using a large dataset with nearly 1 million examples, and it shows big improvements over previous methods. |
Keywords
» Artificial intelligence » Alignment » Mixture of experts
