2 citations · 2 across the 11 of their papers we have counts for
12 papers
ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models
Sajjad Ghiasvand, Yifan Yang, Mahnoosh Alizadeh +1
Prompt tuning adapts vision--language models with few trainable parameters, but computing their gradients still requires backpropagation through frozen encoders. We introduce ZOMP,…
IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents
Yifan Yang, Zhen Zhang, Jiayi Tian +2
This paper investigates reinforcement learning (RL) methods for improving tool-calling capabilities in multimodal small language model (SLM) agents. While existing works have explo…
GRZO: Group-Relative Zeroth-Order Optimization for Large Language Model Fine-Tuning
Liyan Tan, Yequan Zhao, Yifan Yang +3
Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gra…
SharpZO: Hybrid Sharpness-Aware Vision Language Model Prompt Tuning via Forward-Only Passes
Yifan Yang, Zhen Zhang, Rupak Vignesh Swaminathan +3
Fine-tuning vision language models (VLMs) has achieved remarkable performance across various downstream tasks; yet, it requires access to model gradients through backpropagation (B…
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression
Jiayi Tian, Ryan Solgi, Jinming Lu +3
Large Language Models (LLMs) have enabled remarkable progress in natural language processing, yet their high computational and memory demands pose challenges for deployment in reso…
Wanda++: Pruning Large Language Models via Regional Gradients
Yifan Yang, Kai Zhen, Bhavana Ganesh +11
Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy de…
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
Jiajun Zhou, Yifan Yang, Kai Zhen +6
Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-…
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models
Zhen Zhang, Yifan Yang, Kai Zhen +4
Large language models have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning demands significant memory, posing challenges for resource-constrained…
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue +3
Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language Models (LLMs) are trained on data from a single device or client. However, real-world scenarios…
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
Yifan Yang, Kai Zhen, Ershad Banijamal +2
Fine-tuning large language models (LLMs) has achieved remarkable performance across various natural language processing tasks, yet it demands more and more memory as model sizes ke…
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models
Zhuotong Chen, Zihu Wang, Yifan Yang +2
Despite the effectiveness of deep neural networks in numerous natural language processing applications, recent findings have exposed the vulnerability of these language models when…
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
Yifan Yang, Jiajun Zhou, Ngai Wong +1
Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance. However, existing…