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The impressive advances and applications of large language and jointlanguage-and-visual understanding models has led to an increased need formethods of probing their potential reasoning capabilities. However, thedifficulty of gather naturally-occurring data for complex multi-modal reasoningtasks bottlenecks the evaluation of AI methods on tasks which are not alreadycovered by an academic dataset. In this work, we leverage recent advances inhigh resolution text-to-image generation to develop a framework for generatingevaluation data for multi-modal reasoning tasks. We apply this framework togenerate context-dependent anomaly data, creating a synthetic dataset on achallenging task which is not well covered by existing datasets. We benchmarkthe performance of a state-of-the-art visual question answering (VQA) modelagainst data generated with this method, and demonstrate that while the task istractable, the model performs significantly worse on the context-dependentanomaly detection task than on standard VQA tasks.

Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data