Multilingual Reference Need Assessment System for Wikipedia

TL;DR

This paper presents a multilingual machine learning system designed to assist Wikipedia editors by identifying claims that need citations, improving verification efficiency across ten language editions and balancing accuracy with computational constraints.

Contribution

The paper introduces a novel multilingual reference need assessment system for Wikipedia, optimized for real-world deployment and outperforming existing benchmarks.

Findings

Outperforms existing benchmarks in reference need detection

Balances model accuracy with computational efficiency

Deployed in production for real-world use

Abstract

Wikipedia is a critical source of information for millions of users across the Web. It serves as a key resource for large language models, search engines, question-answering systems, and other Web-based applications. In Wikipedia, content needs to be verifiable, meaning that readers can check that claims are backed by references to reliable sources. This depends on manual verification by editors, an effective but labor-intensive process, especially given the high volume of daily edits. To address this challenge, we introduce a multilingual machine learning system to assist editors in identifying claims requiring citations. Our approach is tested in 10 language editions of Wikipedia, outperforming existing benchmarks for reference need assessment. We not only consider machine learning evaluation metrics but also system requirements, allowing us to explore the trade-offs between model accuracy and computational efficiency under real-world infrastructure constraints. We deploy our system in production and release data and code to support further research.