@inproceedings{huang-etal-2026-contextualize,
    title = "How to Contextualize Empirical Data for Risk Analysis with {LLM}s: A Case Study of Power Outages",
    author = "Huang, Haiyun  and
      Li, Yukun  and
      Pretell, Marco A  and
      Naroian, Jacob  and
      Khan, Ebadah  and
      Liu, Liping",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.324/",
    pages = "6158--6172",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Models (LLMs) are increasingly being considered for high-stakes decision-making, yet their application in statistical risk analysis remains largely underexplored. A central challenge in this domain is enabling LLMs to effectively leverage historical data. To address this, we propose novel methods for extracting key information from raw data and translating it into structured contextual input within the LLM prompt. Applying our methods to a case study of power outage risk assessment, we demonstrate that this contextualization strategy significantly improves the LLM{'}s performance in risk assessment tasks. While the LLM{'}s prediction performance still does not match that of a standard machine learning model, the LLM-based approach offers distinct advantages in versatility and interpretability. These findings demonstrate a new paradigm for contextualizing data to support risk assessment."
}