---
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}
}
```