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.

翻译：维基百科是网络上数百万用户的关键信息来源，也是大型语言模型、搜索引擎、问答系统及其他网络应用的重要资源。在维基百科中，内容需具备可验证性，即读者能够核实条目中的论断是否由可靠来源支撑。这一过程依赖于编辑的人工验证，虽然有效但劳动强度大，尤其是在每日大量编辑的情况下。为解决这一挑战，我们提出了一种多语言机器学习系统，以辅助编辑识别需要引用的论断。该系统在10种语言的维基百科版本中进行了测试，在引用需求评估方面超越了现有基准。我们不仅考虑了机器学习评估指标，还结合了系统需求，从而探索了在实际基础设施限制下模型准确性与计算效率之间的权衡。该系统已投入生产环境，并发布了相关数据与代码以支持后续研究。