用大模型辅助政府资金申请评审，提速超两月且误判极少。
Leveraging LLMs to Streamline the Review of Public Funding Applications
- 用大模型自动分析企业海外拓展和居民节能改造两类申请。
- 市民报销类申请评阅效率提升20.1%，误报率极低。
- 适合需要大规模、高时效评审的公共政策执行部门。
每年欧盟及其成员国拨付数百万欧元资助各类发展项目。然而，申请数量激增导致评审环节严重滞后，受限于人力。本文报告了在两项政府项目中部署AI辅助评审的实际成果：（i）企业国际业务拓展申请；（ii）居民节能家居改造费用报销申请。尽管评审流程不同，但结果表明AI显著提升了处理效率并减轻了工作负担。具体而言，在市民报销项目中，解决方案使评审人员生产力提升20.1%，基于测试集的假阳性率可忽略不计。整体评审周期因此缩短超过两个月，凸显了AI驱动自动化在大规模评审流程中的实际价值。
原文摘要 · Abstract (English)
Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for these programs often creates significant bottlenecks in evaluation processes, due to limited human capacity. In this work, we detail the real-world deployment of AI-assisted evaluation within the pipeline of two government initiatives: (i) corporate applications aimed at international business expansion, and (ii) citizen reimbursement claims for investments in energy-efficient home improvements. While these two cases involve distinct evaluation procedures, our findings confirm that AI effectively enhanced processing efficiency and reduced workload across both types of applications. Specifically, in the citizen reimbursement claims initiative, our solution increased reviewer productivity by 20.1%, while keeping a negligible false-positive rate based on our test set observations. These improvements resulted in an overall reduction of more than 2 months in the total evaluation time, illustrating the impact of AI-driven automation in large-scale evaluation workflows.
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