MoHoBench: Assessing Honesty of Multimodal Large Language Models via
  Unanswerable Visual Questions
Authors: name: Yanxu Zhu, name: Shitong Duan, name: Xiangxu Zhang, name: Jitao Sang, name: Peng Zhang, name: Tun Lu, name: Xiao Zhou, name: Jing Yao, name: Xiaoyuan Yi, name: Xing Xie

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' capability to act honestly,
especially when faced with visually unanswerable questions, remains largely
underexplored. This work presents the first systematic assessment of honesty
behaviors across various MLLMs. We ground honesty in models' response behaviors
to unanswerable visual questions, define four representative types of such
questions, and construct MoHoBench, a large-scale MMLM honest benchmark,
consisting of 12k+ visual question samples, whose quality is guaranteed by
multi-stage filtering and human verification. Using MoHoBench, we benchmarked
the honesty of 28 popular MMLMs and conducted a comprehensive analysis. Our
findings show that: (1) most models fail to appropriately refuse to answer when
necessary, and (2) MMLMs' honesty is not solely a language modeling issue, but
is deeply influenced by visual information, necessitating the development of
dedicated methods for multimodal honesty alignment. Therefore, we implemented
initial alignment methods using supervised and preference learning to improve
honesty behavior, providing a foundation for future work on trustworthy MLLMs.
Our data and code can be found at https://github.com/DSTTSD/MoHoBench.