most citedEffective Neural Topic Modeling with Embedding Clustering Regularization

10 citations · 16 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20241 cited

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL202310 cited

Effective Neural Topic Modeling with Embedding Clustering Regularization

Xiaobao Wu, Xinshuai Dong, Thong Nguyen +1

Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semant…

cs.CL20232 cited

Zero-Shot Text Classification via Self-Supervised Tuning

Chaoqun Liu, Wenxuan Zhang, Guizhen Chen +4

Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scal…

cs.CL20233 cited

Fact-Checking Complex Claims with Program-Guided Reasoning

Liangming Pan, Xiaobao Wu, Xinyuan Lu +4

Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Check…