@inproceedings{hayat-etal-2026-numbers,
    title = "From Numbers to Narratives: Efficient Language Model-Based Detection for Safety-Critical Minority Classes",
    author = "Hayat, Ahatsham  and
      Tridle, Hunter  and
      Hasan, Mohammad Rashedul",
    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.258/",
    pages = "4920--4937",
    ISBN = "979-8-89176-386-9",
    abstract = "Safety-critical classification tasks face a persistent challenge: traditional models achieve high overall accuracy but inadequate performance on critical minority classes. We introduce a numbers to narratives framework that transforms tabular data into contextually rich descriptions, enabling language models to leverage pre-trained knowledge for minority class detection. Our approach integrates structured verbalization, linguistically-informed augmentation, and parameter-efficient fine-tuning to address the ``minority class blind spot'' in high-consequence domains. Using a significantly more efficient model architecture than existing approaches, our framework achieves superior minority class F1-scores: 78.76{\%} for machine failures (+7.42 points over XGBoost), 65.87{\%} for at-risk students (+12.12 points over MLP), and 32.00{\%} for semiconductor failures (+1.01 points over XGBoost, despite 14:1 class imbalance). Our approach also improves overall accuracy by up to 22.43{\%} in five of six datasets while maintaining computational feasibility. Ablation studies confirm that narrative-based verbalization enables effective reasoning about tabular data by contextualizing abstract numerical features. This work provides a practical, resource-efficient approach for enhancing minority class performance in safety-critical domains."
}