9 citations · 20 across the 21 of their papers we have counts for
6 papers · 1 filter
LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs
Tian Huang, Tom Bourgeade, Irina Illina
Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students' clinical and communication skills through structured patient interviews.…
Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents
Selim Furkan Tekin, Fatih Ilhan, Gaowen Liu +2
The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive frameworks for dynamically changing env…
Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
Yibo Wang, Tiansheng Huang, Li Shen +6
Harmful fine-tuning attack introduces significant security risks to the fine-tuning services. Main-stream defenses aim to vaccinate the model such that the later harmful fine-tunin…
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…