Publications (6)
Towards the TopMost: A Topic Modeling System Toolkit
Xiaobao Wu, Fengjun Pan, Anh Tuan Luu
Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinc…
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models
Shuai Zhao, Meihuizi Jia, Zhongliang Guo +7
Large Language Models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks, part…
Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection
Fengjun Pan, Xiaobao Wu, Tho Quan +1
Detecting harmful memes is crucial for safeguarding the integrity and harmony of online environments, yet existing detection methods are often resource-intensive, inflexible, and l…
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning
Shuai Zhao, Meihuizi Jia, Luu Anh Tuan +2
In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite…
On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling
Xiaobao Wu, Fengjun Pan, Thong Nguyen +4
Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, ex…
Are LLMs Good Zero-Shot Fallacy Classifiers?
Fengjun Pan, Xiaobao Wu, Zongrui Li +1
Fallacies are defective arguments with faulty reasoning. Detecting and classifying them is a crucial NLP task to prevent misinformation, manipulative claims, and biased decisions.…