244 citations · 1.3k across the 96 of their papers we have counts for
23 papers · 2 filters
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…
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
Xiaobao Wu, Liangming Pan, Yuxi Xie +7
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly co…
Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models
Do Xuan Long, Duong Ngoc Yen, Anh Tuan Luu +3
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language model (LLM) generation. Specifically, it guides…
As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss
Xin Mao, Feng-Lin Li, Huimin Xu +3
Direct Preference Optimization (DPO) has emerged as a more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimizat…
Who's Who: Large Language Models Meet Knowledge Conflicts in Practice
Quang Hieu Pham, Hoang Ngo, Anh Tuan Luu +1
Retrieval-augmented generation (RAG) methods are viable solutions for addressing the static memory limits of pre-trained language models. Nevertheless, encountering conflicting sou…
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.…
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
Siyue Zhang, Anh Tuan Luu, Chen Zhao
Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have y…
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…
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts t…
Discrete Diffusion Language Model for Efficient Text Summarization
Do Huu Dat, Do Duc Anh, Anh Tuan Luu +1
While diffusion models excel at conditional generating high-quality images, prior works in discrete diffusion models were not evaluated on conditional long-text generation. In this…
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…
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…
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models
Chaoqun Liu, Wenxuan Zhang, Yiran Zhao +2
Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. While prior works have leverag…
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges
Bosheng Ding, Chengwei Qin, Ruochen Zhao +7
In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training ex…
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
Xin Mao, Feng-Lin Li, Huimin Xu +2
While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding…
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…
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
Zhiyuan Hu, Chumin Liu, Xidong Feng +6
In the face of uncertainty, the ability to *seek information* is of fundamental importance. In many practical applications, such as medical diagnosis and troubleshooting, the infor…
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…
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment
Cong-Duy Nguyen, The-Anh Vu-Le, Thong Nguyen +2
Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning. However, due to differences in…
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…
LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
Khoi M. Le, Trinh Pham, Tho Quan +1
Paraphrases are texts that convey the same meaning while using different words or sentence structures. It can be used as an automatic data augmentation tool for many Natural Langua…