# Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

arXiv: https://arxiv.org/abs/2404.09764 · HTML: https://mlsanity.com/paper/2404.09764/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2024.

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

- [Inverse Knowledge Search over Verifiable Reasoning: Synthesizing a Scientific Encyclopedia from a Long Chains-of-Thought Knowledge Base](https://mlsanity.com/paper/2510.26854/) (2025)
- [Making Large Language Models Speak Tulu: Structured Prompting for an Extremely Low-Resource Language](https://mlsanity.com/paper/2602.15378/) (2026)
- [Multilingual Reference Need Assessment System for Wikipedia](https://mlsanity.com/paper/2603.17146/) (2026)
- [Language-Agnostic Modeling of Source Reliability on Wikipedia](https://mlsanity.com/paper/2410.18803/) (2024)
- [A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia](https://mlsanity.com/paper/2410.19150/) (2024)
- [On the effective transfer of knowledge from English to Hindi Wikipedia](https://mlsanity.com/paper/2412.05708/) (2024)

## Abstract (arXiv)

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.