# Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing

arXiv: https://arxiv.org/abs/2410.08918 · HTML: https://mlsanity.com/paper/2410.08918/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [Wikipedia in the Era of LLMs: Evolution and Risks](https://mlsanity.com/paper/2503.02879/) (2025)
- [BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions](https://mlsanity.com/paper/2506.05766/) (2025)
- [TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices](https://mlsanity.com/paper/2605.31113/) (2026)
- [Language-Agnostic Modeling of Source Reliability on Wikipedia](https://mlsanity.com/paper/2410.18803/) (2024)
- [Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages](https://mlsanity.com/paper/2605.31136/) (2026)
- [Temporal Preference Optimization for Unsupervised Retrieval](https://mlsanity.com/paper/2606.17664/) (2026)

## Abstract (arXiv)

Wikimedia content is used extensively by the AI community and within the language modeling community in particular. In this paper, we provide a review of the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We point to opportunities for greater use of Wikimedia content but also identify ways in which the language modeling community could better center the needs of Wikimedia editors. In particular, we call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.