2 citations · 4 across the 4 of their papers we have counts for
4 papers
Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging
Lin Lu, Zhigang Zuo, Ziji Sheng +1
Model merging has emerged as a promising approach for updating large language models (LLMs) by integrating multiple domain-specific models into a cross-domain merged model. Despite…
Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack
Chenxi Dai, Lin Lu, Pan Zhou
Decentralized training has become a resource-efficient framework to democratize the training of large language models (LLMs). However, the privacy risks associated with this framew…
Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models
Chengzhe Feng, Yanan Sun, Ke Li +3
As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive.…
Exploring the Robustness of Decentralized Training for Large Language Models
Lin Lu, Chenxi Dai, Wangcheng Tao +3
Decentralized training of large language models has emerged as an effective way to democratize this technology. However, the potential threats associated with this approach have no…