Judge Before Answer: Can MLLM Discern the False Premise in Question?
This work addresses a critical robustness issue in MLLMs for AI safety and reliability, though it is incremental as it builds on existing benchmarks.
The paper tackles the problem of multimodal large language models (MLLMs) being vulnerable to false premise questions by introducing a comprehensive benchmark (JBA dataset) and an enhancement framework, resulting in significant improvements in recognition accuracy.
Multimodal large language models (MLLMs) have witnessed astonishing advancements in recent years. Despite these successes, MLLMs remain vulnerable to flase premise problems. However, existing benchmarks targeting this issue are limited in scope: they often lack fine-grained categorization, exhibit insufficient coverage, and thus fail to provide a rigorous evaluation of the ability of models to recognize false premises. To bridge this gap, we introduce a fully automated pipeline for constructing a comprehensive benchmark of false premise questions. Our method systematically categorizes the premises into three main types and thirteen subtypes according to the abilities required to identify the premises, resulting in the JBA dataset.Results show current MLLMs still struggle with false premise recognition. Building upon this benchmark, we further propose a recognition enhancement framework tailored to strengthen the robustness of MLLMs to detect false premises. Extensive experiments demonstrate that models trained with our framework achieve significant improvements in false premise recognition.