AAAI 2026technical0 citations
MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions
Yanxu Zhu, Shitong Duan, Xiangxu Zhang, Jitao Sang, Peng Zhang, Tun Lu, Xiao Zhou, Jing Yao
Abstract
Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs
BibTeX
@inproceedings{aaai2026_mohobenchassessi,
title = {MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions},
author = {Yanxu Zhu and Shitong Duan and Xiangxu Zhang and Jitao Sang and Peng Zhang and Tun Lu and Xiao Zhou and Jing Yao and Xiaoyuan Yi and Xing Xie},
booktitle = {AAAI 2026},
year = {2026}
}