Papers by Zongyuan Ge
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding (2025.coling-main)
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| Challenge: | Object categories are typically organized into a multi-granularity taxonomic hierarchy . traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. |
| Approach: | They propose a framework that combines vision-language models with a deeper exploitation of the hierarchy. |
| Outcome: | The proposed framework shows significant improvements on 11 diverse visual recognition benchmarks. |
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)
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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao
| Challenge: | Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions. |
| Approach: | They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models. |
| Outcome: | The proposed benchmarks show that the models perform better in open-ended conversations. |
Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses (2026.acl-long)
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Chongyuan Dai, Yaling Shen, Zihan Gao, Jia Li, Yishun Jiang, Yaxiong Wang, Liu Liu, Zongyuan Ge, Jinpeng Hu
| Challenge: | Culture is a fundamental determinant of human affective processing and affective perceptions are often limited by declarative knowledge or established societal customs. |
| Approach: | They propose a multimodal benchmark that leverages LLM-generated provisional labels to isolate cross-cultural emotional distinctions. |
| Outcome: | The proposed benchmark captures cross-cultural emotional distinctions and derives reliable ground-truth annotations through human evaluation. |
PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics (2026.findings-acl)
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Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge
| Challenge: | Mental disorders affect nearly one in seven people worldwide, yet the vast majority do not receive adequate care. |
| Approach: | They propose a framework to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations. |
| Outcome: | Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness. |
WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification (2025.emnlp-main)
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| Challenge: | Existing MCoT methods focus on inter-object reasoning, overlooking intra-object understanding crucial for image classification. |
| Approach: | They propose a Weak-supervision-guided Step-by-step Explanation method that reformulates MCoTs under weak supervision into concise, interpretable reasoning chains. |
| Outcome: | The proposed method improves interpretability by 37% and improves classification accuracy. |
TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification (2026.findings-acl)
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Jianghao Wu, Feilong Tang, Yulong Li, Ming Hu, Haochen Xue, Shoaib Jameel, Zongyuan Ge, Yutong Xie, Imran Razzak
| Challenge: | Existing efforts to improve medical question answering performance follow two directions. |
| Approach: | They propose a framework that combines a generalist with a domain-specific specialist without any model fine-tuning or parameter updates. |
| Outcome: | The proposed framework boosts GPT-4o accuracy by 13.8%, deepseek-R1 by 16.8%, and improves a vanilla 7B model from 14.1% to 23.9%. |
CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations (2026.eacl-short)
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Stephanie Fong, Zimu Wang, Guilherme C Oliveira, Xiangyu Zhao, Yiwen Jiang, Jiahe Liu, Beau-Luke Colton, Scott W. Woods, Martha Shenton, Barnaby Nelson, Zongyuan Ge, Dominic Dwyer
| Challenge: | Psychotic disorders are a major contributor to the global health burden due to their relatively high mortality risk. |
| Approach: | They propose an NLP pipeline that takes semi-structured clinical interviews to predict psychosis risk and generate novel SHAP explanation formats. |
| Outcome: | The proposed pipeline outperforms baseline models and achieves 90% accuracy across three BERT variants. |
Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models (2025.acl-long)
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| Challenge: | Concept Bottleneck Models (CBMs) map visual representations to a set of humanunderstandable textual concepts, which are then interpreted by a linear combination of these concept scores. |
| Approach: | They propose a dynamic, agent-based approach that adjusts the concept bank in response to environmental feedback, optimizing the number of concepts for sufficiency yet concise coverage. |
| Outcome: | The proposed model improves classification accuracy by 6% and interpretability assessments by 30%. |