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.

翻译：简短描述是维基百科用户体验的关键组成部分，但其在不同语言和主题间的覆盖仍不均衡。在先前工作中，我们提出了Descartes——一种用于生成简短描述的多语言模型。本报告展示了Descartes在维基百科安卓应用中的试点部署结果：编辑者在修改简短描述时，系统会提供基于Descartes输出的建议。该实验涵盖12种语言，涉及超过3,900篇文章及375位编辑者参与。总体而言，90%被采纳的Descartes描述在质量评分（5分制）中达到3分及以上，其平均评分与人工撰写的描述相当。编辑者既直接采纳也修改采纳了机器建议，而撤销率与问题报告率均保持较低水平。本次试点还揭示了实际部署中需考量的因素，包括响应延迟、特定语言差距以及对敏感主题设置防护机制的必要性。这些结果表明，在技术、设计和社区防护措施到位的前提下，Descartes生成的简短描述能够有效协助编辑者减少内容缺口。