  Multimodal Large Language Models (MLLMs) are typically assessed using
expensive annotated multimodal benchmarks, which often lag behind the rapidly
evolving demands of MLLM evaluation. This paper outlines and validates
GenCeption, a novel, annotation-free evaluation method that requires only
unimodal data to measure inter-modality semantic coherence and inversely
assesses MLLMs' tendency to hallucinate. This approach eliminates the need for
costly data annotation, minimizes the risk of training data contamination,
results in slower benchmark saturation, and avoids the illusion of emerging
abilities. Inspired by the DrawCeption game, GenCeption begins with a
non-textual sample and proceeds through iterative description and generation
steps. The semantic drift across iterations is quantified using the GC@T
metric. Based on the GenCeption method, we establish the MMECeption benchmark
for evaluating Vision LLMs (VLLMs), and compare performance of several popular
VLLMs and human annotators. Our empirical results validate GenCeption's
effectiveness, demonstrating strong correlations with established VLLM
benchmarks. VLLMs still significantly lack behind human performance and
struggle especially with text-intensive tasks.
