1 citations · 1 across the 24 of their papers we have counts for
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HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering
An Nguyen Phu, Dung Nguyen Quang, Luu Hieu An +3
Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a complex question are usuall…
TALAS: Teacher-Anchored Layer Alignment with Adaptive Sharpness-Aware Minimization for Embedding Distillation
Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi +4
Knowledge Distillation (KD) has established itself as a pivotal technique for compressing large pre-trained language models. However, existing methods that force a student to stric…
TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
Truong Nguyen, Tien-Phat Nguyen, Linh Ngo Van +3
Direct Preference Optimization (DPO) is a widely used RL-free method for aligning language models from pairwise preferences, but it models preferences over full sequences even thou…
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van +3
Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or we…
MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation
Pham Khanh Chi, Quoc Phong Dao, Thuat Nguyen +3
Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers or token-level outputs, igno…
SRA: Span Representation Alignment for Large Language Model Distillation
Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi +4
Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ different tokenizers. While exi…
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining
Phung Gia Huy, Hai An Vu, Minh-Phuc Truong +4
Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) of…
CTPD: Cross Tokenizer Preference Distillation
Truong Nguyen, Phi Van Dat, Ngan Nguyen +3
While knowledge distillation has seen widespread use in pre-training and instruction tuning, its application to aligning language models with human preferences remains underexplore…
WAVE++: Capturing Within-Task Variance for Continual Relation Extraction with Adaptive Prompting
Bao-Ngoc Dao, Minh Le, Quang Nguyen +3
Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE). However, storing examples from previous tasks increases memory usage and raises privac…
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers
Anh Duc Le, Tu Vu, Nam Le Hai +4
Large Language Models (LLMs) achieve state-of-the-art performance across various NLP tasks but face deployment challenges due to high computational costs and memory constraints. Kn…
Few-Shot, No Problem: Descriptive Continual Relation Extraction
Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran +3
Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional mem…
GloCOM: A Short Text Neural Topic Model via Global Clustering Context
Quang Duc Nguyen, Tung Nguyen, Duc Anh Nguyen +3
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemm…
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Minh Le, Tien Ngoc Luu, An Nguyen The +5
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring n…
Lifelong Event Detection via Optimal Transport
Viet Dao, Van-Cuong Pham, Quyen Tran +3
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performan…
Preserving Generalization of Language models in Few-shot Continual Relation Extraction
Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh +4
Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled da…
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
Duy-Tung Pham, Thien Trang Nguyen Vu, Tung Nguyen +3
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and t…
ToVo: Toxicity Taxonomy via Voting
Tinh Son Luong, Thanh-Thien Le, Thang Viet Doan +3
Existing toxic detection models face significant limitations, such as lack of transparency, customization, and reproducibility. These challenges stem from the closed-source nature…
Realistic Evaluation of Toxicity in Large Language Models
Tinh Son Luong, Thanh-Thien Le, Linh Ngo Van +1
Large language models (LLMs) have become integral to our professional workflows and daily lives. Nevertheless, these machine companions of ours have a critical flaw: the huge amoun…