Papers by Yuchang Su
NegVQA: Can Vision Language Models Understand Negation? (2025.findings-acl)
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| Challenge: | NegVQA is a visual question answering (VQA) benchmark consisting of 7,379 two-choice questions covering diverse negation scenarios and image-question distributions. |
| Approach: | They propose a visual question answering benchmark consisting of 7,379 two-choice questions covering diverse negation scenarios and image-question distributions. |
| Outcome: | The proposed model fails to correctly interpret negation, leading to critical errors in interactive AI systems. |