Summary of Synchart: Synthesizing Charts From Language Models, by Mengchen Liu et al.
SynChart: Synthesizing Charts from Language Models
by Mengchen Liu, Qixiu Li, Dongdong Chen, Dong Chen, Jianmin Bao, Yunsheng Li
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 paper explores ways to build advanced models for multi-modality tasks like chart understanding using large language models (LLMs) alone. It creates a large-scale chart dataset called SynChart and trains a 4.2B model on this data, achieving near-GPT-4O performance on the ChartQA task, surpassing GPT-4V. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make advanced computer models using just big language models, which are already very good at understanding text. It makes a huge dataset of different charts with lots of information and uses it to train a new model that’s really good at understanding those charts. This new model is almost as good as the best ones out there! |
Keywords
» Artificial intelligence » Gpt
