@inproceedings{tripathi-etal-2025-confidence,
    title = "The Confidence Paradox: Can {LLM} Know When It{'}s Wrong?",
    author = "Tripathi, Sahil  and
      Nafis, MD Tabrez  and
      Hussain, Imran  and
      Gao, Jiechao",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.113/",
    pages = "2078--2087",
    ISBN = "979-8-89176-298-5",
    abstract = "Document Visual Question Answering (DocVQA) models often produce overconfident or ethically misaligned responses, especially under uncertainty. Existing models like LayoutLMv3, UDOP, and DONUT focus on accuracy but lack ethical calibration. We propose HonestVQA, a model-agnostic, self-supervised framework that aligns model confidence with correctness using weighted loss and contrastive learning. We introduce two new metrics{---}Honesty Score (H-Score) and Ethical Confidence Index (ECI){---}to evaluate ethical alignment. HonestVQA improves accuracy and F1 by up to 4.3{\%} across SpDocVQA, InfographicsVQA, and SROIE datasets, while reducing overconfidence. It also generalizes well across domains, achieving 78.9{\%} accuracy and 76.1{\%} F1-score."
}