activity
20202026
most citedPreserving Generalization of Language models in Few-shot Continual Relation Extraction

1 citations · 1 across the 24 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.LG2025

Sharpness-Guided Group Relative Policy Optimization via Probability Shaping

Tue Le, Linh Ngo Van, Trung Le

Reinforcement learning with verifiable rewards (RLVR) has become a practical route to improve large language model reasoning, and Group Relative Policy Optimization (GRPO) is a wid…

cs.CV2025

Hierarchical Neural Collapse Detection Transformer for Class Incremental Object Detection

Duc Thanh Pham, Hong Dang Nguyen, Nhat Minh Nguyen Quoc +3

Recently, object detection models have witnessed notable performance improvements, particularly with transformer-based models. However, new objects frequently appear in the real wo…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…