@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://preview.aclanthology.org/ingest-emnlp/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."
}