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Yaling Shen (沈雅龄) I am a second-year PhD student at the Monash AIM for Health Lab, supervised by A/Prof. Zongyuan Ge, and Prof. Gholamreza (Reza) Haffari. Previously, I received my Master's degree from the Technical University of Munich, where I conducted my thesis under the supervision of Prof. Nassir Navab. Before that, I completed my Bachelor's degree from the Chinese University of Hong Kong, Shenzhen, working closely with Prof. Xiang Wan at Shenzhen Research Institute of Big Data (SRIBD). My research focus is on AI in healthcare, specifically the development of large language models (LLMs) and multimodal large language models (MLLMs) for the medical domain. |
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News
[07-2026] CEDAR received the SAC Highlight Award at ACL 2026! |
Publications |
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Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses
Chongyuan Dai*, Yaling Shen*, Zihan Gao, Jia Li, Yishun Jiang, Yaxiong Wang, Liu Liu, Zongyuan Ge, Jinpeng Hu, ACL, 2026 (SAC Highlight Award) arXiv / Code CEDAR is a multimodal benchmark of culturally elicited distinct affective responses, revealing that culturally grounded affective understanding remains a significant challenge for current LLMs. |
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PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics
Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge ACL Findings, 2026 arXiv / Code PsychEthicsBench is the first principle-grounded benchmark based on Australian psychology and psychiatry guidelines, evaluating LLMs' ethical knowledge and behavior in mental health contexts beyond refusal-based safety metrics. |
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OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Ming Hu*, Kun Yuan*, Yaling Shen*, Feilong Tang, Xiaohao Xu, Lin Zhou, Wei Li, Ying Chen, Zhongxing Xu, Zelin Peng, Siyuan Yan, Vinkle Srivastav, Diping Song, Tianbin Li, Danli Shi, Jin Ye, Nicolas Padoy, Nassir Navab, Junjun He, Zongyuan Ge ICCV, 2025 arXiv / Code The OphCLIP model is built upon the OphVL dataset, which is a large-scale and comprehensive collection of over 375k hierarchically structured video-text pairs with tens of thousands of different combinations of attributes. |
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Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Yaling Shen*, Zhixiong Zhuang*, Kun Yuan, Maria-Irina Nicolae, Nassir Navab, Nicolas Padoy, Mario Fritz AAAI, 2025 (Oral Presentation) arXiv / Blog / Code Adversarial Domain Alignment (ADA-Steal) is the first stealing attack against medical multimodal large language models without any access to medical data. |
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Cross-modal memory networks for radiology report generation
Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan, Tsung-Hui Chang ACL, 2021 arXiv / Code Cross-modal memory networks (CMN) are proposed to enhance the encoder-decoder framework for radiology report generation. |
Academic Service
Workshop Organizer: The First Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy (MultiPsyche) at AACL 2026. |
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