@inproceedings{marques-etal-2025-leveraging,
title = "Leveraging {LLM}s to Streamline the Review of Public Funding Applications",
author = "Marques, Jo{\~a}o DS and
Duarte, Andre Vicente and
de Carvalho, Andr{\'e} Mendes Marques and
Rocha, Gil and
Martins, Bruno and
Oliveira, Arlindo L.",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.143/",
pages = "2041--2060",
ISBN = "979-8-89176-333-3",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Leveraging LLMs to Streamline the Review of Public Funding Applications
%A Marques, João DS
%A Duarte, Andre Vicente
%A de Carvalho, André Mendes Marques
%A Rocha, Gil
%A Martins, Bruno
%A Oliveira, Arlindo L.
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F marques-etal-2025-leveraging
%X 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.
%U https://aclanthology.org/2025.emnlp-industry.143/
%P 2041-2060
Markdown (Informal)
[Leveraging LLMs to Streamline the Review of Public Funding Applications](https://aclanthology.org/2025.emnlp-industry.143/) (Marques et al., EMNLP 2025)
ACL
- João DS Marques, Andre Vicente Duarte, André Mendes Marques de Carvalho, Gil Rocha, Bruno Martins, and Arlindo L. Oliveira. 2025. Leveraging LLMs to Streamline the Review of Public Funding Applications. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2041–2060, Suzhou (China). Association for Computational Linguistics.