Summary of Gui-world: a Dataset For Gui-oriented Multimodal Llm-based Agents, by Dongping Chen et al.
GUI-WORLD: A Dataset for GUI-oriented Multimodal LLM-based Agents
by Dongping Chen, Yue Huang, Siyuan Wu, Jingyu Tang, Liuyi Chen, Yilin Bai, Zhigang He, Chenlong Wang, Huichi Zhou, Yiqiang Li, Tianshuo Zhou, Yue Yu, Chujie Gao, Qihui Zhang, Yi Gui, Zhen Li, Yao Wan, Pan Zhou, Jianfeng Gao, Lichao Sun
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 This paper introduces a new challenge for multimodal large language models (MLLMs) in understanding graphical user interfaces (GUIs). Specifically, it focuses on the ability of MLLMs to perceive temporal information and comprehend various GUI scenarios. The researchers create a new dataset, GUI-World, which features human-annotated examples of six GUI scenarios and eight types of questions. They then evaluate the performance of current state-of-the-art MLLMs, including ImageLLMs and VideoLLMs, on this dataset. Their findings show that while VideoLLMs can improve understanding of certain GUI tasks, they still struggle with dynamic GUI content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers can better understand and interact with graphical user interfaces (GUIs). Right now, computers are good at understanding static things like pictures or text, but they’re not great at understanding things that change over time, like a website loading new information. To help computers get better at this, the researchers created a special dataset called GUI-World. They then tested some of the most advanced computer models on this dataset and found that while they can do some tasks well, they still struggle with changing information. |
