5 papers
Label Shift Aware Adaptation for Online Zero-shot Learning with Contrastive Language-Image Pre-Training (CLIP)
Pengxiao Han, Changkun Ye, Yanshuo Wang +5
Vision-language models like Contrastive Language-Image Pre-Training (CLIP) have been extensively studied in data-scarce scenarios. A particularly challenging and realistic task in…
Identity-Robust Language Model Generation via Content Integrity Preservation
Miao Zhang, Kelly Chen, Md Mehrab Tanjim +1
Large Language Model (LLM) outputs often vary across user sociodemographic attributes, leading to disparities in factual accuracy, utility, and safety, even for objective questions…
Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
Dongkyu Cho, Miao Zhang, Rumi Chunara
Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs…
Enhancing Diffusion-based Dataset Distillation via Adversary-Guided Curriculum Sampling
Lexiao Zou, Gongwei Chen, Yanda Chen +1
Dataset distillation aims to encapsulate the rich information contained in dataset into a compact distilled dataset but it faces performance degradation as the image-per-class (IPC…
MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models
Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
As Large Language Models (LLMs) grow dramatically in size, there is an increasing trend in compressing and speeding up these models. Previous studies have highlighted the usefulnes…