GUIDE: Graphical User Interface Data for Execution
Not on the map. Why?
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
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2023Visual Instruction Tuningcited by 7,792
- 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
- 2015VQA: Visual Question Answeringcited by 2,489
- 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
- 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
- 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,311
- 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,114
- 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
- 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 702
- 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
11 more
- 2020TextCaps: a Dataset for Image Captioning with Reading Comprehensioncited by 398
- 2020Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformerscited by 288
- 2023BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questionscited by 125
- 2024MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learningcited by 66
- 2024Correctness Comparison of ChatGPT-4, Gemini, Claude-3, and Copilot for Spatial Taskscited by 10
- 2020Robotic Process Automation -- A Systematic Literature Review and Assessment Frameworkcited by 9
- 2023Separate and Locate: Rethink the Text in Text-based Visual Question Answeringcited by 7
- 2024Veagle: Advancements in Multimodal Representation Learningcited by 5
- 2023Multiple-Question Multiple-Answer Text-VQAcited by 4
- 2022DeeperDive: The Unreasonable Effectiveness of Weak Supervision in Document Understanding A Case Study in Collaboration with UiPath Inccited by 2
- 2024AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automationcited by 1
Built on by
Citing papers per year: 2024: 4; 2025: 2; 2026: 1.
7 papers.
- 2024Large Language Model-Brained GUI Agents: A Surveycited by 160
- 2024Ferret-UI 2: Mastering Universal User Interface Understanding Across Platformscited by 51
- 2024GUI Action Narrator: Where and When Did That Action Take Place?cited by 9
- 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AIcited by 8
- 2024V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLMcited by 6
- 2025InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Managementcited by 2
- 2026Software Engineering for and with GUI Agentcited by 1
- 2026Software Engineering for and with GUI Agent10 Aug 2026
- 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI19 Oct 2025
- 2025InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management17 Sep 2025
- 2024Large Language Model-Brained GUI Agents: A Survey27 Nov 2024
- 2024Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms24 Oct 2024
- 2024GUI Action Narrator: Where and When Did That Action Take Place?19 Jun 2024
- 2024V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM24 May 2024
Similar, not cited
Close in wording, no citation either way
- 2025ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction
- 2025GUIrilla: A Scalable Framework for Automated Desktop UI Exploration
- 2025AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs
- 2025TongUI: Internet-Scale Trajectories from Multimodal Web Tutorials for Generalized GUI Agents
- 2026Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
- 2026Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
- 2025ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction
- 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agents