mlsanityData to 7 Oct 2026

GUIDE: Graphical User Interface Data for Execution

arXiv 2404.16048 ↗First posted 9 Apr 2024cs.HC cs.AIcited by 7

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Abstract (arXiv)

In this paper, we introduce GUIDE, a novel dataset tailored for the advancement of Multimodal Large Language Model (MLLM) applications, particularly focusing on Robotic Process Automation (RPA) use cases. Our dataset encompasses diverse data from various websites including Apollo(62.67%), Gmail(3.43%), Calendar(10.98%) and Canva(22.92%). Each data entry includes an image, a task description, the last action taken, CoT and the next action to be performed along with grounding information of where the action needs to be executed. The data is collected using our in-house advanced annotation tool NEXTAG (Next Action Grounding and Annotation Tool). The data is adapted for multiple OS, browsers and display types. It is collected by multiple annotators to capture the variation of design and the way person uses a website. Through this dataset, we aim to facilitate research and development in the realm of LLMs for graphical user interfaces, particularly in tasks related to RPA. The dataset's multi-platform nature and coverage of diverse websites enable the exploration of cross-interface capabilities in automation tasks. We believe that our dataset will serve as a valuable resource for advancing the capabilities of multi-platform LLMs in practical applications, fostering innovation in the field of automation and natural language understanding. Using GUIDE, we build V-Zen, the first RPA model to automate multiple websites using our in-House Automation tool AUTONODE

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 23 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

23 of 23 references.

References · 23

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11 more
  1. 2020TextCaps: a Dataset for Image Captioning with Reading Comprehensioncited by 398
  2. 2020Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformerscited by 288
  3. 2023BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questionscited by 125
  4. 2024MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learningcited by 66
  5. 2024Correctness Comparison of ChatGPT-4, Gemini, Claude-3, and Copilot for Spatial Taskscited by 10
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  7. 2023Separate and Locate: Rethink the Text in Text-based Visual Question Answeringcited by 7
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction
  2. 2025GUIrilla: A Scalable Framework for Automated Desktop UI Exploration
  3. 2025AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs
  4. 2025TongUI: Internet-Scale Trajectories from Multimodal Web Tutorials for Generalized GUI Agents
  5. 2026Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
  6. 2026Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
  7. 2025ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction
  8. 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agents