14 citations · 125 across the 88 of their papers we have counts for
12 papers · 1 filter
UniGuard: Multi-View Guardrail Learning for Cross-Interface Safety Generalization
Viet-Thanh Pham, Thuy-Trang Vu, Junae Kim +4
Guardrail models are increasingly used as safety layers for LLMs and agentic systems, but most are trained on fixed prompt or response interfaces and become unreliable when the sam…
Neural Topic Modeling with Large Language Models in the Loop
Xiaohao Yang, He Zhao, Weijie Xu +4
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have d…
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
Xiaohao Yang, He Zhao, Dinh Phung +2
Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are e…
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri +4
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers. However, previous research on e…
Systematic Assessment of Factual Knowledge in Large Language Models
Linhao Luo, Thuy-Trang Vu, Dinh Phung +1
Previous studies have relied on existing question-answering benchmarks to evaluate the knowledge stored in large language models (LLMs). However, this approach has limitations rega…
Towards Generalising Neural Topical Representations
Xiaohao Yang, He Zhao, Dinh Phung +1
Topic models have evolved from conventional Bayesian probabilistic models to recent Neural Topic Models (NTMs). Although NTMs have shown promising performance when trained and test…
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Thuy-Trang Vu, Shahram Khadivi, Xuanli He +2
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT). This paper investigates whether the domain information can be transf…
Two-view Graph Neural Networks for Knowledge Graph Completion
Vinh Tong, Dai Quoc Nguyen, Dinh Phung +1
We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowl…
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
Dai Quoc Nguyen, Vinh Tong, Dinh Phung +1
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completio…
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering
Quan Tran, Nhan Dam, Tuan Lai +4
Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests. Previou…
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest. Current self-supervised adaptatio…
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen +1
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head…