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ttn0011/pageguide_guide_data

PageGuide Dataset This repository contains the dataset for PageGuide, a browser extension that assists users in navigating webpages and locating information by grounding LLM answers directly in the HTML DOM. Project Page: pageguide.github.io Paper: PageGuide: Browser extension to assist users in navigating a webpage and locating information Code: github.com/tin-xai/pageguide Dataset Description The PageGuide evaluation utilizes several distinct datasets… See the full description on the dataset page: https://huggingface.co/datasets/ttn0011/pageguide_guide_data.

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PageGuide Dataset

This repository contains the dataset for PageGuide, a browser extension that assists users in navigating webpages and locating information by grounding LLM answers directly in the HTML DOM.

Dataset Description

The PageGuide evaluation utilizes several distinct datasets representing different tasks:

  1. 1.`pageguide_userstudy`: Raw interaction logs from the user study — completion times, chat transcripts, correctness labels, paired statistical results, and post-study survey responses.
  2. 2.`pageguide_find_data`: 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.
  3. 3.`pageguide_guide_data`: 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.
  4. 4.`pageguide_hide_data`: 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

You can load these datasets using the Hugging Face datasets library:

User Study Data

python
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()

Find Task Data

python
from datasets import load_dataset
find_tasks = load_dataset("ttn0011/pageguide_find_data", split="train").to_pandas()

Guide Task Data

python
from datasets import load_dataset
guide_tasks = load_dataset("ttn0011/pageguide_guide_data", split="train").to_pandas()

Hide Task Data

python
from datasets import load_dataset
hide_tasks = load_dataset("ttn0011/pageguide_hide_data", split="train").to_pandas()