The Confidence Paradox: Can LLM Know When It's Wrong

• URL: http://arxiv.org/abs/2506.23464v2
• Date: Tue, 28 Oct 2025 10:19:32 GMT
• Title: The Confidence Paradox: Can LLM Know When It's Wrong
• Authors: Sahil Tripathi, Md Tabrez Nafis, Imran Hussain, Jiechao Gao,
• Abstract summary: Document Visual Question Answering (DocVQA) models often produce overconfident or ethically misaligned responses.<n>We propose HonestVQA, a model-agnostic, self-supervised framework that aligns model confidence with correctness using weighted loss and contrastive learning.
• Score: 5.445980143646736
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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