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dataset_info:
- config_name: paw_edits
features:
- name: text
dtype: string
- name: page
dtype: string
- name: section_name
dtype: string
- name: is_paw_edit
dtype: bool
- config_name: queries
features:
- name: query
dtype: string
- name: type
dtype: string
license: cc-by-4.0
task_categories:
- text-classification
tags:
- animal-welfare
- wikipedia
- data-attribution
- llm-training
---
# PAW Wikipedia Attribution Dataset
Data for the paper "Small edits, large models: How Wikipedia advocacy shapes LLM values" (Navas, Brazilek & Gnauck, 2026).
## Files
### paw_edits.csv (236 rows)
Wikipedia sections used in training data attribution experiments. Each row is a text chunk from Wikipedia.
| Column | Description |
|--------|-------------|
| `text` | The Wikipedia section text |
| `page` | Wikipedia article title |
| `section_name` | Section heading |
| `is_paw_edit` | Whether this section was added/edited by Pro-Animal Wikipedians |
118 PAW-edited sections (animal welfare content added by advocacy editors) paired with 118 control sections from the same Wikipedia articles.
### queries.csv (170 rows)
Queries used to measure training data influence.
| Column | Description |
|--------|-------------|
| `query` | The query text |
| `type` | `animal_welfare` (80 queries) or `general` (90 queries) |
Animal welfare queries ask about animal welfare at specific companies/by specific politicians. General queries ask about the same entities without mentioning animal welfare.
## Citation
```bibtex
@article{navas2026small,
title={Small edits, large models: How Wikipedia advocacy shapes LLM values},
author={Navas, M and Brazilek, J and Gnauck, A},
year={2026}
}
```
|