用AI生成维基短描述，提升编辑效率与内容覆盖率。
Integrating Machine-Generated Short Descriptions into the Wikipedia Android App: A Pilot Deployment of Descartes
- 引入多语言AI模型Descartes，为编辑提供短描述建议。
- 90%采纳建议质量达3分以上，接近人工水平。
- 适合希望提升维基内容完整性的编辑与项目团队。
短描述是维基百科用户体验的关键部分，但其覆盖度在不同语言和主题间仍不均衡。此前我们提出了多语言模型Descartes用于生成短描述。本报告展示在维基百科Android应用中对Descartes的试点部署结果：编辑在修改短描述时可获得基于Descartes输出的建议。实验涵盖12种语言，涉及超过3,900篇文章及375名编辑。总体来看，90%被采纳的描述质量评分至少为3分（满分5分），平均评分与人工撰写相当。编辑既直接采纳也进行修改，且回滚和举报率保持低位。试点还揭示了部署中的实际挑战，包括延迟、语言差异及敏感话题防护需求。结果表明，在技术、设计与社区规范协同下，Descartes能有效缓解内容缺口问题。
原文摘要 · Abstract (English)
Short descriptions are a key part of the Wikipedia user experience, but their coverage remains uneven across languages and topics. In previous work, we introduced Descartes, a multilingual model for generating short descriptions. In this report, we present the results of a pilot deployment of Descartes in the Wikipedia Android app, where editors were offered suggestions based on outputs from Descartes while editing short descriptions. The experiment spanned 12 languages, with over 3,900 articles and 375 editors participating. Overall, 90% of accepted Descartes descriptions were rated at least 3 out of 5 in quality, and their average ratings were comparable to human-written ones. Editors adopted machine suggestions both directly and with modifications, while the rate of reverts and reports remained low. The pilot also revealed practical considerations for deployment, including latency, language-specific gaps, and the need for safeguards around sensitive topics. These results indicate that Descartes's short descriptions can support editors in reducing content gaps, provided that technical, design, and community guardrails are in place.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。