@article{padro-etal-2026-llm,
    title = "An {LLM}-Based Assistant for Debt Waiver Court Procedures",
    author = "Padro, Lluis  and
      Ferr{\'e}s, Daniel  and
      Saur{\'i}, Roser  and
      Artigot, Mireia",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.35/",
    pages = "505--514",
    abstract = "Spanish Insolvency Law 1/2020 of the 5th of May enables individuals to apply for debt waiver under certain conditions. The large number of applications submitted each year places a heavy burden on judges and court officers, who must examine heterogeneous documentation before issuing a ruling. This paper presents an AI-based assistant designed to support the processing of debt waiver cases. The system integrates PDF-to-text conversion, rule-based document classification, large language model (LLM)-based information extraction, and post-processing to consolidate fragmented or duplicated records. A front-end interface provides structured summaries of the application content, and can automatically generate draft rulings. Evaluated on a set of real applications, the system achieves over 92{\%} F1 in document classification and up to 91{\%} F1 in personal data extraction, showing the potential of open-source LLMs to reduce administrative workload and accelerate judicial procedures, while keeping the final decision with the judge."
}