9 citations · 20 across the 20 of their papers we have counts for
3 papers · 2 filters
H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang +4
The alignment of pre-trained LLMs continues to draw significant attention from both industry and academia, aiming to ensure responses that are helpful, harmless, and honest. Howeve…
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…
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Tiansheng Huang, Sihao Hu, Fatih Ilhan +2
Harmful fine-tuning attack poses serious safety concerns for large language models' fine-tuning-as-a-service. While existing defenses have been proposed to mitigate the issue, thei…