5 papers
Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models
Dang Nguyen, Jiping Li, Jinghao Zheng +1
Synthetically augmenting training datasets with diffusion models has become an effective strategy for improving the generalization of image classifiers. However, existing approache…
Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories
Nilay Naharas, Dang Nguyen, Nesihan Bulut +3
Data-efficient learning aims to eliminate redundancy in large training datasets by training models on smaller subsets of the most informative examples. While data selection has bee…
Synthetic Text Generation for Training Large Language Models via Gradient Matching
Dang Nguyen, Zeman Li, Mohammadhossein Bateni +3
Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text genera…
Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
Dang Nguyen, Ali Payani, Baharan Mirzasoleiman
Hallucination in large language models (LLMs) can be detected by assessing the uncertainty of model outputs, typically measured using entropy. Semantic entropy (SE) enhances tradit…
Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures
Dang Nguyen, Wenhan Yang, Rathul Anand +2
Training with larger mini-batches improves the convergence rate and can yield superior performance. However, training with large mini-batches becomes prohibitive for Large Language…