自动识别维基百科需引用的陈述，提升多语言内容可信度。
Multilingual Reference Need Assessment System for Wikipedia
- 基于机器学习模型，跨10种语言自动判断内容是否需引用。
- 在多语言维基百科中表现优于现有基准，准确率显著提升。
- 兼顾精度与效率，适合实际部署，开源数据代码供研究使用。
维基百科是全球数百万用户的重要信息来源，也是大语言模型、搜索引擎和问答系统的关键资源。为确保内容可验证性，所有声明需有可靠来源支持，这依赖编辑手动核查，工作量巨大，尤其面对每日海量编辑。为此，我们提出一种多语言机器学习系统，辅助编辑识别需要引用的陈述。该方法在10种语言版本的维基百科上测试，性能超越现有基准。我们不仅评估模型在机器学习指标上的表现，还考虑实际系统需求，在真实基础设施约束下权衡模型精度与计算效率。系统已投入生产使用，并公开数据与代码，推动后续研究。
原文摘要 · Abstract (English)
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.
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