metadata
task_categories:
- other
license: mit
PageGuide Dataset
This repository contains dataset artifacts from the paper PageGuide: Browser extension to assist users in navigating a webpage and locating information.
- Project Page: https://pageguide.github.io/
- Repository: https://github.com/tin-xai/pageguide
- Paper: Hugging Face Paper Page
Dataset Description
PageGuide was evaluated in a controlled within-subjects user study. The evaluation data is structured into four datasets:
1. pageguide_userstudy
- Purpose: Raw interaction logs from the user study — completion times, chat transcripts, correctness labels, paired statistical results, and post-study survey responses.
- Sample Usage:
from datasets import load_dataset
tasks = load_dataset("ttn0011/pageguide_userstudy", data_files="tasks.csv", split="train").to_pandas()
paired = load_dataset("ttn0011/pageguide_userstudy", data_files="paired_times.csv", split="train").to_pandas()
2. pageguide_find_data
- Purpose: Task stimuli for the Find condition — 10 real webpages (NASA, Wikipedia, Cleveland Clinic, WWF, Britannica, JMLR) each annotated with up to 2 factual questions, ground-truth answers, and supporting evidence spans.
- Sample Usage:
from datasets import load_dataset
find_tasks = load_dataset("ttn0011/pageguide_find_data", split="train").to_pandas()
3. pageguide_guide_data
- Purpose: Task stimuli for the Guide condition — 7 procedural tasks across 6 platforms (Google Sheets, Google Docs, Google Slides, Coda, TradingView, Scratch), labelled Easy or Medium difficulty.
- Sample Usage:
from datasets import load_dataset
guide_tasks = load_dataset("ttn0011/pageguide_guide_data", split="train").to_pandas()
4. pageguide_hide_data
- Purpose: Task stimuli for the Hide condition — 37 annotated webpage snapshots (Amazon, Netflix, TechCrunch, Allrecipes, Spotify, Yelp, and more) with
(user_goal, hide_query, difficulty, hidden_elements)annotations and ground-truth CSS selectors. - Sample Usage:
from datasets import load_dataset
hide_tasks = load_dataset("ttn0011/pageguide_hide_data", split="train").to_pandas()