14 citations · 21 across the 26 of their papers we have counts for
7 papers · 1 filter
LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
Wei Huang, Anda Cheng, Yinggui Wang +2
Large Language Models (LLMs) can be fine-tuned on domain-specific data to enhance their performance in specialized fields. However, such data often contains numerous low-quality sa…
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning
Wei Huang, Anda Cheng, Yinggui Wang
Recent advancements in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tunin…
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment
Wei Huang, Anda Cheng, Zhao Zhang +1
Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language mo…
Information Leakage from Embedding in Large Language Models
Zhipeng Wan, Anda Cheng, Yinggui Wang +1
The widespread adoption of large language models (LLMs) has raised concerns regarding data privacy. This study aims to investigate the potential for privacy invasion through input…
A Fast, Performant, Secure Distributed Training Framework For Large Language Model
Wei Huang, Yinggui Wang, Anda Cheng +3
The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data. However, maliciously stealing model parameters and data from the s…
UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang +2
Personalized federated learning has gained significant attention as a promising approach to address the challenge of data heterogeneity. In this paper, we address a relatively unex…
Transferable Adversarial Examples with Bayes Approach
Mingyuan Fan, Cen Chen, Wenmeng Zhou +1
The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without…