ConfLVLM introduces a statistical framework to guarantee factuality in Large Vision-Language Models, addressing critical reliability concerns in image-based text generation.

• Reduces hallucinations by providing confidence scores for generated content
• Improves reliability in high-stakes domains including medical imaging interpretation
• Enhances trustworthiness by ensuring generated text aligns with visual information
• Offers statistical guarantees rather than just improvements in average performance

This research is particularly valuable for medical applications where accurate radiology report generation can improve diagnostic efficiency while maintaining clinical safety standards. The confidence-aware framework helps prevent potentially harmful misinterpretations in healthcare settings.

Towards Statistical Factuality Guarantee for Large Vision-Language Models