85 citations · 225 across the 32 of their papers we have counts for
11 papers · 1 filter
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Rui Cao, Ming Shan Hee, Adriel Kuek +3
Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to f…
ChatGPT and Bard Responses to Polarizing Questions
Abhay Goyal, Muhammad Siddique, Nimay Parekh +9
Recent developments in natural language processing have demonstrated the potential of large language models (LLMs) to improve a range of educational and learning outcomes. Of recen…
Decoding the Underlying Meaning of Multimodal Hateful Memes
Ming Shan Hee, Wen-Haw Chong, Roy Ka-Wei Lee
Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable e…
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
Nirmalendu Prakash, Ming Shan Hee, Roy Ka-Wei Lee
Total Defence is a defence policy combining and extending the concept of military defence and civil defence. While several countries have adopted total defence as their defence pol…
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Lei Wang, Wanyu Xu, Yihuai Lan +4
Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT)…
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Zhiqiang Hu, Roy Ka-Wei Lee, Nancy F. Chen
Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled d…
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
Han Wang, Ming Shan Hee, Md Rabiul Awal +2
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 ar…
CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis
Jie Gao, Kenny Tsu Wei Choo, Junming Cao +2
While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA)-a process that involves multiple researchers…
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Zhiqiang Hu, Lei Wang, Yihuai Lan +6
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetunin…
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection
Md Rabiul Awal, Roy Ka-Wei Lee, Eshaan Tanwar +2
Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have expl…
Prompting for Multimodal Hateful Meme Classification
Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong +1
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external kn…