---
license: mit
task_categories:
- other
---

# 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](https://pageguide.github.io/)
* **Paper:** [PageGuide: Browser extension to assist users in navigating a webpage and locating information](https://huggingface.co/papers/2604.23772)
* **Code:** [github.com/tin-xai/pageguide](https://github.com/tin-xai/pageguide)

## Dataset Description

The PageGuide evaluation utilizes several distinct datasets representing different tasks:

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. **`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. **`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. **`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()
```