@inproceedings{majumder-etal-2025-typical,
title = "Not Your Typical Government Tipline: {LLM}-Assisted Routing of Environmental Protection Agency Citizen Tips",
author = "Majumder, Sharanya and
Li, Zehua and
Ouyang, Derek and
Rodolfa, Kit T and
Eneva, Elena and
Nyarko, Julian and
Ho, Daniel E.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1788/",
pages = "35295--35303",
ISBN = "979-8-89176-332-6",
abstract = "Regulatory agencies often operate with limited resources and rely on tips from the public to identify potential violations. However, processing these tips at scale presents significant operational challenges, as agencies must correctly identify and route relevant tips to the appropriate enforcement divisions. Through a case study, we demonstrate how advances in large language models can be utilized to support overburdened agencies with limited capacities. In partnership with the U.S. Environmental Protection Agency, we leverage previously unstudied citizen tips data from their ``Report a Violation'' system to develop an LLM-assisted pipeline for tip routing. Our approach filters out 80.5{\%} of irrelevant tips and increases overall routing accuracy from 31.8{\%} to 82.4{\%} compared to the current routing system. At a time of increased focus on government efficiencies, our approach provides a constructive path forward by using technology to empower civil servants."
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<abstract>Regulatory agencies often operate with limited resources and rely on tips from the public to identify potential violations. However, processing these tips at scale presents significant operational challenges, as agencies must correctly identify and route relevant tips to the appropriate enforcement divisions. Through a case study, we demonstrate how advances in large language models can be utilized to support overburdened agencies with limited capacities. In partnership with the U.S. Environmental Protection Agency, we leverage previously unstudied citizen tips data from their “Report a Violation” system to develop an LLM-assisted pipeline for tip routing. Our approach filters out 80.5% of irrelevant tips and increases overall routing accuracy from 31.8% to 82.4% compared to the current routing system. At a time of increased focus on government efficiencies, our approach provides a constructive path forward by using technology to empower civil servants.</abstract>
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%0 Conference Proceedings
%T Not Your Typical Government Tipline: LLM-Assisted Routing of Environmental Protection Agency Citizen Tips
%A Majumder, Sharanya
%A Li, Zehua
%A Ouyang, Derek
%A Rodolfa, Kit T.
%A Eneva, Elena
%A Nyarko, Julian
%A Ho, Daniel E.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F majumder-etal-2025-typical
%X Regulatory agencies often operate with limited resources and rely on tips from the public to identify potential violations. However, processing these tips at scale presents significant operational challenges, as agencies must correctly identify and route relevant tips to the appropriate enforcement divisions. Through a case study, we demonstrate how advances in large language models can be utilized to support overburdened agencies with limited capacities. In partnership with the U.S. Environmental Protection Agency, we leverage previously unstudied citizen tips data from their “Report a Violation” system to develop an LLM-assisted pipeline for tip routing. Our approach filters out 80.5% of irrelevant tips and increases overall routing accuracy from 31.8% to 82.4% compared to the current routing system. At a time of increased focus on government efficiencies, our approach provides a constructive path forward by using technology to empower civil servants.
%U https://aclanthology.org/2025.emnlp-main.1788/
%P 35295-35303
Markdown (Informal)
[Not Your Typical Government Tipline: LLM-Assisted Routing of Environmental Protection Agency Citizen Tips](https://aclanthology.org/2025.emnlp-main.1788/) (Majumder et al., EMNLP 2025)
ACL