Automated Generation of Challenging Multiple-Choice Questions for Vision
  Language Model Evaluation
Authors: name: Yuhui Zhang, name: Yuchang Su, name: Yiming Liu, name: Xiaohan Wang, name: James Burgess, name: Elaine Sui, name: Chenyu Wang, name: Josiah Aklilu, name: Alejandro Lozano, name: Anjiang Wei, name: Ludwig Schmidt, name: Serena Yeung-Levy

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.