2026
JUDGE BEFORE ANSWER: CAN MLLM DISCERN THE FALSE PREMISE IN QUESTION?
ICASSP 2026poster
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 i…