Publications

Ming Shan Hee, Roy Ka-Wei Lee (2025). Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions. arXiv.
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Yuhao Wu, Ming Shan Hee, Zhiqing Hu, Roy Ka-Wei Lee (2025). LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs. arXiv.
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Ming Shan Hee, Shivam Sharma, Rui Cao, Palash Nandi, Preslav Nakov, Tanmoy Chakraborty, Roy Ka-Wei Lee (2024). Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models. Findings of the Association for Computational Linguistics: EMNLP 2024.
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Ming Shan Hee, Aditi Kumaresan, Roy Ka-Wei Lee (2024). Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.
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Ri Chi Ng, Nirmalendu Prakash, Ming Shan Hee, Kenny Tsu Wei Choo, Roy Ka-Wei Lee (2024). SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore. Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024).
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Nirmalendu Prakash, Han Wang, Nguyen Khoi Hoang, Ming Shan Hee, Roy Ka-Wei Lee (2023). PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models. Proceedings of the 31st ACM International Conference on Multimedia.
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Rui Cao, Ming Shan Hee, Adriel Kuek, Wen-Haw Chong, Roy Ka-Wei Lee, Jing Jiang (2023). Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection. Proceedings of the 31st ACM International Conference on Multimedia.
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Han Wang, Ming Shan Hee, Md Rabiul Awal, Kenny Tsu Wei Choo, Roy Ka-Wei Lee (2023). Evaluating GPT-3 Generated Explanations for Hateful Content Moderation. arXiv.
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Nirmalendu Prakash, Ming Shan Hee, Roy Ka-Wei Lee (2023). TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore. Proceedings of the 14th ACM Multimedia Systems Conference.
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Ming Shan Hee, Wen-Haw Chong, Roy Ka-Wei Lee (2023). Decoding the Underlying Meaning of Multimodal Hateful Memes. arXiv.
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