Voxel51/mind2web_multimodal_test_task

Dataset Card for Multimodal Mind2Web "Cross-Task" Test Split

Note: This dataset is the test split of the Cross-Task dataset introduced in the paper.

This is a FiftyOne dataset with 1338 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/mind2web_multimodal_test_task")

# Launch the App
session = fo.launch_app(dataset)

Dataset Description

Curated by: The Ohio State University NLP Group (OSU-NLP-Group)
Shared by: OSU-NLP-Group on Hugging Face
Language(s) (NLP): en
License: OPEN-RAIL License

Dataset Source

Repository: https://github.com/OSU-NLP-Group/SeeAct and https://huggingface.co/datasets/osunlp/Multimodal-Mind2Web
Paper: "GPT-4V(ision) is a Generalist Web Agent, if Grounded" by Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su
Demo: https://osu-nlp-group.github.io/SeeAct

Uses

Direct Use

• Evaluating web agents' ability to generalize to new tasks on familiar websites
• Benchmarking LMMs and LLMs on web navigation tasks
• Training and fine-tuning models for web navigation
• Testing model performance on tasks that require following multi-step instructions

Out-of-Scope Use

• Developing web agents for harmful purposes (as stated in the paper's impact statement)
• Automating actions that could violate website terms of service
• Creating agents that access users' personal profiles or perform sensitive operations without consent

Dataset Structure

• Contains 177 tasks across 17 domains and 64 websites
• Tasks average 7.6 actions each
• Average 4,172 visual tokens per task
• Average 607 HTML elements per task
• Average 123,274 HTML tokens per task
• Each example includes task descriptions, HTML structure, operations (CLICK, TYPE, SELECT), target elements with attributes, and action histories

FiftyOne Dataset Structure

Basic Info: 1,338 web UI screenshots with task-based annotations

Core Fields:

• action_uid: StringField - Unique action identifier
• annotation_id: StringField - Annotation identifier
• target_action_index: IntField - Index of target action in sequence
• ground_truth: EmbeddedDocumentField(Detection) - Element to interact with:
  • label: Action type (TYPE, CLICK)
  • bounding_box: a list of relative bounding box coordinates in [0, 1] in the following format: <top-left-x>, <top-left-y>, <width>, <height>]
  • target_action_reprs: String representation of target action
• website: EmbeddedDocumentField(Classification) - Website name
• domain: EmbeddedDocumentField(Classification) - Website domain category
• subdomain: EmbeddedDocumentField(Classification) - Website subdomain category
• task_description: StringField - Natural language description of the task
• full_sequence: ListField(StringField) - Complete sequence of actions for the task
• previous_actions: ListField - Actions already performed in the sequence
• current_action: StringField - Action to be performed
• alternative_candidates: EmbeddedDocumentField(Detections) - Other possible elements

Dataset Creation

Curation Rationale

The Cross-Task split was specifically designed to evaluate an agent's ability to generalize to new tasks on websites and domains it has already encountered during training.

Source Data

Data Collection and Processing

• Based on the original MIND2WEB dataset
• Each HTML document is aligned with its corresponding webpage screenshot image
• Underwent human verification to confirm element visibility and correct rendering for action prediction

Who are the source data producers?

Web screenshots and HTML were collected from 64 websites across 17 domains that were also represented in the training data.

Annotations

Annotation process

Each task includes annotated action sequences showing the correct steps to complete the task. These were likely captured through a tool that records user actions on websites.

Who are the annotators?

Researchers from The Ohio State University NLP Group or hired annotators, though specific details aren't provided in the paper.

Personal and Sensitive Information

The dataset focuses on non-login tasks to comply with user agreements and avoid privacy issues.

Bias, Risks, and Limitations

• Performance on this split is generally better than Cross-Website and Cross-Domain, as models can leverage knowledge of website structures
• Supervised fine-tuning methods show advantages on this split compared to in-context learning
• The dataset may contain biases present in the original websites
• Website layouts and functionality may change over time, affecting the validity of the dataset

Citation

BibTeX:

@article{zheng2024seeact,
  title={GPT-4V(ision) is a Generalist Web Agent, if Grounded},
  author={Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024},
  url={https://openreview.net/forum?id=piecKJ2DlB},
}

@inproceedings{deng2023mindweb,
  title={Mind2Web: Towards a Generalist Agent for the Web},
  author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=kiYqbO3wqw}
}

APA:

Zheng, B., Gou, B., Kil, J., Sun, H., & Su, Y. (2024). GPT-4V(ision) is a Generalist Web Agent, if Grounded. arXiv preprint arXiv:2401.01614.

Dataset Card Contact

GitHub: https://github.com/OSU-NLP-Group/SeeAct