6 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
Fatih Ilhan, Selim Furkan Tekin, Tiansheng Huang +6
Fine-tuning pre-trained large language models (LLMs) has become a common practice for personalized natural language understanding (NLU) applications on downstream tasks and domain-…
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang, Sihao Hu, Ling Liu
The new paradigm of finetuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the finetuning t…
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we…