@inproceedings{ranjit-etal-2026-uncovering,
    title = "Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants",
    author = "Ranjit, Jaspreet  and
      Cho, Hyundong Justin  and
      Smerdon, Claire J.  and
      Nam, Yoonsoo  and
      Phung, Myles  and
      May, Jonathan  and
      Blosnich, John R.  and
      Swayamdipta, Swabha",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.688/",
    pages = "15072--15092",
    ISBN = "979-8-89176-390-6",
    abstract = "The National Violent Death Reporting System (NVDRS) documents suicides in the United States. In a demanding public health data pipeline, annotators manually extract structured information from death investigation records following extensive codebooks (i.e. annotation guidelines) painstakingly developed by experts. In this work, we facilitate data-driven insights from the NVDRS data to support the development of novel suicide interventions by leveraging language models (LM) as assistants to these (a) data annotators and (b) experts. We find that LM predictions match existing data annotations about 85{\%} of the time across 50 NVDRS variables. Where the LM disagrees with existing annotations, our expert review identifies that 38{\%} of these instances reveal inconsistencies between narratives and structured data. Finally, we introduce a human-in-the-loop algorithm that helps experts efficiently build and refine codebooks for new variables by having them only focus on providing feedback for incorrect LM predictions. We apply our algorithm to a real-world case study, and find that about 96K narratives contain evidence of victim interactions with legal professionals, which surfaces a substantial opportunity for upstream intervention that is not captured in the original structured data. Our findings provide evidence that LMs can serve as effective assistants to public health researchers who handle sensitive data in high-stakes scenarios."
}