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    <abstract>Advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive performance in image-conditioned text generation; however, hallucinated outputs–text that misaligns with the visual input–pose a major barrier to their use in safety-critical applications. We introduce ConfLVLM, a conformal-prediction-based framework that achieves finite-sample distribution-free statistical guarantees to the factuality of LVLM output. Taking each generated detail as a hypothesis, ConfLVLM statistically tests factuality via efficient heuristic uncertainty measures to filter out unreliable claims. We conduct extensive experiments covering three representative application domains: general scene understanding, medical radiology report generation, and document understanding. Remarkably, ConfLVLM reduces the error rate of claims generated by LLaVa-1.5 for scene descriptions from 87.8% to 10.0% by filtering out erroneous claims with a 95.3% true positive rate. Our results further show that ConfLVLM is highly flexible, and can be applied to any black-box LVLMs paired with any uncertainty measure for any image-conditioned free-form text generation task while providing a rigorous guarantee on controlling hallucination risk.</abstract>
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%0 Conference Proceedings
%T Towards Statistical Factuality Guarantee for Large Vision-Language Models
%A Li, Zhuohang
%A Yan, Chao
%A Jackson, Nicholas J.
%A Cui, Wendi
%A Li, Bo
%A Zhang, Jiaxin
%A Malin, Bradley A.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-towards-statistical
%X Advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive performance in image-conditioned text generation; however, hallucinated outputs–text that misaligns with the visual input–pose a major barrier to their use in safety-critical applications. We introduce ConfLVLM, a conformal-prediction-based framework that achieves finite-sample distribution-free statistical guarantees to the factuality of LVLM output. Taking each generated detail as a hypothesis, ConfLVLM statistically tests factuality via efficient heuristic uncertainty measures to filter out unreliable claims. We conduct extensive experiments covering three representative application domains: general scene understanding, medical radiology report generation, and document understanding. Remarkably, ConfLVLM reduces the error rate of claims generated by LLaVa-1.5 for scene descriptions from 87.8% to 10.0% by filtering out erroneous claims with a 95.3% true positive rate. Our results further show that ConfLVLM is highly flexible, and can be applied to any black-box LVLMs paired with any uncertainty measure for any image-conditioned free-form text generation task while providing a rigorous guarantee on controlling hallucination risk.
%U https://aclanthology.org/2025.emnlp-main.576/
%P 11446-11467

Markdown (Informal)

[Towards Statistical Factuality Guarantee for Large Vision-Language Models](https://aclanthology.org/2025.emnlp-main.576/) (Li et al., EMNLP 2025)

• Towards Statistical Factuality Guarantee for Large Vision-Language Models (Li et al., EMNLP 2025)

ACL

• Zhuohang Li, Chao Yan, Nicholas J Jackson, Wendi Cui, Bo Li, Jiaxin Zhang, and Bradley A. Malin. 2025. Towards Statistical Factuality Guarantee for Large Vision-Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 11446–11467, Suzhou, China. Association for Computational Linguistics.