6 papers
Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
Minh Le, Anh Nguyen, Huy Nguyen +3
Visual Prompt Tuning (VPT) has proven effective for parameter-efficient adaptation of pre-trained vision models to downstream tasks by inserting task-specific learnable prompt toke…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
Nghiem T. Diep, Huy Nguyen, Chau Nguyen +5
The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance.…
RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
Tuan Truong, Chau Nguyen, Huy Nguyen +3
Low-rank Adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale foundation models. Despite its popularity, the theoretical understanding of LoRA has remaine…
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Minh Le, Chau Nguyen, Huy Nguyen +3
Prompt-based techniques, such as prompt-tuning and prefix-tuning, have gained prominence for their efficiency in fine-tuning large pre-trained models. Despite their widespread adop…
UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
Reza Basiri, Ali Abedi, Chau Nguyen +2
Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and…