3 papers
cs.LG2025
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-…
cs.CR2025
Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Recent research shows that Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- models lose their safety alignment ability after fine-tuning on a few harmf…
cs.CL2024
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang +2
Combining large language models during training or at inference time has shown substantial performance gain over component LLMs. This paper presents LLM-TOPLA, a diversity-optimize…