Voxel51/mind2web_multimodal_test_website

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

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

This is a FiftyOne dataset with 1019 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_website")

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

Dataset Details for "Cross-Website" Split in Multimodal Mind2Web

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 (mentioned in the Impact Statements section)

Dataset Sources

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 websites within familiar domains
• Testing website-level transfer capabilities of models
• Benchmarking adaptability to new website interfaces with similar functionality
• Assessing how models handle design variations within the same domain category

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 142 tasks across 9 domains and 10 websites
• Tasks average 7.2 actions each
• Average 4,653 visual tokens per task (highest among the three splits)
• Average 612 HTML elements per task (most complex pages among the splits)
• Average 114,358 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-Website split was specifically designed to evaluate an agent's ability to generalize to new websites within domains it has encountered during training, representing a medium difficulty generalization scenario.

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
• Specifically includes 10 new websites from the top-level domains represented in the training data

Who are the source data producers?

Web screenshots and HTML were collected from 10 websites across 9 domains that were represented in the training data, but the specific websites were held out.

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

• This split presents a medium difficulty generalization scenario, testing adaptation to new interfaces within familiar domains
• In-context learning methods show advantages over supervised fine-tuning on this split
• The pages in this split are the most complex in terms of HTML elements and have the highest average visual tokens
• Website layouts and functionality may change over time, affecting the validity of the dataset
• Limited to only 10 websites across 9 domains, may not capture the full diversity of websites within those domains

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