Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation

• URL: http://arxiv.org/abs/2501.03225v1
• Date: Mon, 06 Jan 2025 18:57:31 GMT
• Title: Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
• Authors: Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, Serena Yeung-Levy,
• Abstract summary: We introduce Auto, an agentic framework that automatically converts open-ended questions into multiple-choice format. Using Auto, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format. We evaluate 33 state-of-the-art vision language models on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation.
• Score: 69.81654421834989
• License:
• Abstract: The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce AutoConverter, an agentic framework that automatically converts these open-ended questions into multiple-choice format, enabling objective evaluation while reducing the costly question creation process. Our experiments demonstrate that AutoConverter can generate correct and challenging multiple-choice questions, with VLMs demonstrating consistently similar or lower accuracy on these questions compared to human-created ones. Using AutoConverter, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format, totaling 9,018 questions. We comprehensively evaluate 33 state-of-the-art VLMs on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation.

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