Papers by Serena Yeung-Levy
NegVQA: Can Vision Language Models Understand Negation? (2025.findings-acl)
Copied to clipboard
| 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. |
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR (2026.eacl-long)
Copied to clipboard
James Burgess, Jan N. Hansen, Duo Peng, Yuhui Zhang, Alejandro Lozano, Min Woo Sun, Emma Lundberg, Serena Yeung-Levy
| Challenge: | Recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). |
| Approach: | They propose to train search agents to search and reason over scientific papers and a factoid QA dataset with 60k biomedical paper abstracts. |
| Outcome: | The proposed model outperforms non-RL retrieval baselines and is scalable and extendable to other scientific domains. |
Data or Language Supervision: What Makes CLIP Better than DINO? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs) but it remains unclear whether this advantage stems from CLIP’s language supervision or its much larger training data. |
| Approach: | Embedding analysis shows CLIP captures high-level semantics while DINO is more responsive to low-level features like colors and styles. |
| Outcome: | Embedding analysis shows that CLIP captures high-level semantics, while DINO is more responsive to low-level features like colors and styles. |