From the 1 of 18 linked papers with an AI index.
5 papers · 1 filter
GradPruner: Gradient-Guided Layer Pruning Enabling Efficient Fine-Tuning and Inference for LLMs
Wei Huang, Anda Cheng, Yinggui Wang
Fine-tuning Large Language Models (LLMs) with downstream data is often considered time-consuming and expensive. Structured pruning methods are primarily employed to improve the inf…
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
Wei Huang, Huang Wei, Yinggui Wang
Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selectin…
GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
Kai Yao, Zhaorui Tan, Penglei Gao +7
The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yieldi…
PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models
Guangwei Li, Yuansen Zhang, Yinggui Wang +3
The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is bec…
Privacy Evaluation Benchmarks for NLP Models
Wei Huang, Yinggui Wang, Cen Chen
By inducing privacy attacks on NLP models, attackers can obtain sensitive information such as training data and model parameters, etc. Although researchers have studied, in-depth,…