9 papers
Expert-Guided Forecast Editing for Time-Series Foundation Models
Hung Le, Minh Hoang Nguyen, Manh Nguyen +2
Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate…
SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning
Dai Do, Manh Nguyen, Svetha Venkatesh +1
Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods require extensive data and compute, ma…
Distance Is All You Need: Radial Dispersion for Uncertainty Estimation in Large Language Models
Manh Nguyen, Sunil Gupta, Hung Le
Detecting uncertainty in large language models (LLMs) is essential for building reliable systems, yet many existing approaches are overly complex and depend on brittle semantic clu…
Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation
Manh Nguyen, Sunil Gupta, Hung Le
Ensuring truthfulness in large language models (LLMs) remains a critical challenge for reliable text generation. While supervised fine-tuning and reinforcement learning with human…
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…