117 citations · 150 across the 36 of their papers we have counts for
14 papers · 1 filter
Curriculum Demonstration Selection for In-Context Learning
Duc Anh Vu, Nguyen Tran Cong Duy, Xiaobao Wu +4
Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to eli…
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Thong Thanh Nguyen, Xiaobao Wu, Yi Bin +3
To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to repre…
Multi-Scale Contrastive Learning for Video Temporal Grounding
Thong Thanh Nguyen, Yi Bin, Xiaobao Wu +4
Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video understanding. To encode video moment…
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.…
Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels
Chaoqun Liu, Qin Chao, Wenxuan Zhang +4
Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by…
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
Thong Nguyen, Yi Bin, Xiaobao Wu +6
Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perf…
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
Xiaobao Wu, Xinshuai Dong, Liangming Pan +2
Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend analysis and opinion mining. However, existing models…
Encoding and Controlling Global Semantics for Long-form Video Question Answering
Thong Thanh Nguyen, Zhiyuan Hu, Xiaobao Wu +3
Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems. Previous methods adaptively select frames and regions from long videos…
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
Xiaobao Wu, Thong Nguyen, Delvin Ce Zhang +2
Topic models have been evolving rapidly over the years, from conventional to recent neural models. However, existing topic models generally struggle with either effectiveness, effi…
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning
Cong-Duy Nguyen, Thong Nguyen, Xiaobao Wu +1
Previous work on multimodal sentence embedding has proposed multimodal contrastive learning and achieved promising results. However, by taking the rest of the batch as negative sam…
AKEW: Assessing Knowledge Editing in the Wild
Xiaobao Wu, Liangming Pan, William Yang Wang +1
Knowledge editing injects knowledge updates into language models to keep them correct and up-to-date. However, its current evaluations deviate significantly from practice: their kn…
Topic Modeling as Multi-Objective Contrastive Optimization
Thong Nguyen, Xiaobao Wu, Xinshuai Dong +3
Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the…
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…
A Survey on Neural Topic Models: Methods, Applications, and Challenges
Xiaobao Wu, Thong Nguyen, Anh Tuan Luu
Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various ap…