5 papers
Graph Contrastive Learning via Spectral Graph Alignment
Manh Nguyen
Given augmented views of each input graph, contrastive learning methods (e.g., InfoNCE) optimize pairwise alignment of graph embeddings across views while providing no mechanism to…
Nonnegative Matrix Factorization through Cone Collapse
Manh Nguyen, Daniel Pimentel-Alarcón
Nonnegative matrix factorization (NMF) is a widely used tool for learning parts-based, low-dimensional representations of nonnegative data, with applications in vision, text, and b…
Uncertainty-Guided Checkpoint Selection for Reinforcement Finetuning of Large Language Models
Manh Nguyen, Dung Nguyen, Dai Do +2
Reinforcement learning (RL) finetuning is crucial to aligning large language models (LLMs), but the process is notoriously unstable and exhibits high variance across model checkpoi…
Probabilities Are All You Need: A Probability-Only Approach to Uncertainty Estimation in Large Language Models
Manh Nguyen, Sunil Gupta, Hung Le
Large Language Models (LLMs) exhibit strong performance across various natural language processing (NLP) tasks but remain vulnerable to hallucinations, generating factually incorre…
GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation
Manh Nguyen, Sunil Gupta, Dai Do +1
Hallucination mitigation remains a persistent challenge for large language models (LLMs), even as model scales grow. Existing approaches often rely on external knowledge sources, s…