Ming Shan Hee — accepted AI-conference papers

2025

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung +86

ACL 2025long

Despite Southeast Asia’s (SEA) extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language (VL) research, resulting in AI models that inadequately capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative de…

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2025

LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs

Yuhao Wu, Ming Shan Hee, Zhiqiang Hu, Roy Ka-Wei Lee

ICLR 2025poster

Current benchmarks like ``$\textit{Needle-in-a-Haystack}$'' ($\textit{NIAH}$), $\textit{Ruler}$, and $\textit{Needlebench}$ focus on models' ability to understand long-context input sequences but fail to capture a critical dimension: the generation of high-quality long-form text. Applications such a…

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2025

Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore’s Low-Resource Languages

Yujia Hu, Ming Shan Hee, Preslav Nakov, Roy Ka-Wei Lee

EMNLP 2025

The advancement of Large Language Models (LLMs) has transformed natural language processing; however, their safety mechanisms remain under-explored in low-resource, multilingual settings. Here, we aim to bridge this gap. In particular, we introduce SGToxicGuard, a novel dataset and evaluation framew

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2024

Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning

Ming Shan Hee, Aditi Kumaresan, Roy Ka-Wei Lee

EMNLP 2024main

The widespread presence of hate speech on the internet, including formats such as text-based tweets and multimodal memes, poses a significant challenge to digital platform safety. Recent research has developed detection models tailored to specific modalities; however, there is a notable gap in trans…

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2024

Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models

Ming Shan Hee, Shivam Sharma, Rui Cao, Palash Nandi, Preslav Nakov, Tanmoy Chakraborty +1

EMNLP 2024finding

Moderating hate speech (HS) in the evolving online landscape is a complex challenge, compounded by the multimodal nature of digital content. This survey examines recent advancements in HS moderation, focusing on the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) i…

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2023

Decoding the Underlying Meaning of Multimodal Hateful Memes

Ming Shan Hee, Wen-Haw Chong, Roy Ka-Wei Lee

IJCAI 2023poster

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of ex…

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2023

Evaluating GPT-3 Generated Explanations for Hateful Content Moderation

Han Wang, Ming Shan Hee, Md Rabiul Awal, Kenny Tsu Wei Choo, Roy Ka-Wei Lee

IJCAI 2023poster

Recent research has focused on using large language models (LLMs) to generate explanations for hate speech through fine-tuning or prompting. Despite the growing interest in this area, these generated explanations' effectiveness and potential limitations remain poorly understood. A key concern is tha…

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