TUBench: Benchmark Large Vision-Language Models on Trustworthiness With Unanswerable Questions

Summary

Researchers introduce TUBench, a benchmark that tests Large Vision-Language Models on unanswerable questions built from natural images, code snippets, geometry diagrams and statistical tables. They evaluated 39 foundational models, and GPT-5 reached the highest average accuracy at 83.2% in judging answerability. Manual analysis found GPT-5 gave both a correct answer and explanation in only 63% of cases, with hallucinations causing 87.9% of its incorrect explanations.

Topics

• Multimodal models

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