12 citations · 35 across the 23 of their papers we have counts for
6 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-…
Adversarial Attention Perturbations for Large Object Detection Transformers
Zachary Yahn, Selim Furkan Tekin, Fatih Ilhan +5
Adversarial perturbations are useful tools for exposing vulnerabilities in neural networks. Existing adversarial perturbation methods for object detection are either limited to att…
A Neurosymbolic Agent System for Compositional Visual Reasoning
Yichang Xu, Gaowen Liu, Ramana Rao Kompella +5
The advancement in large language models (LLMs) and large vision models has fueled the rapid progress in multi-modal vision-language reasoning capabilities. However, existing visio…
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
Tiansheng Huang, Sihao Hu, Fatih Ilhan +4
Safety alignment is an important procedure before the official deployment of a Large Language Model (LLM). While safety alignment has been extensively studied for LLM, there is sti…
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…
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…