MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions

TL;DR

This paper introduces MoHoBench, a large-scale benchmark for evaluating honesty in multimodal large language models when faced with visually unanswerable questions, revealing significant honesty issues and proposing initial alignment solutions.

Contribution

It is the first systematic assessment of honesty in MMLMs using a comprehensive benchmark and proposes initial methods to improve honesty behavior.

Findings

Most models fail to refuse unanswerable questions properly.

Honesty in MMLMs is influenced by visual information, not just language.

Proposed alignment methods improve honesty responses.

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' 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/yanxuzhu/MoHoBench.