3 papers
cs.AI2026
TopoTuner: Topological Finetuning of Large Language Models
Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad +4
Full fine-tuning remains a strong way to adapt pretrained LLMs, but it updates all weights and can be expensive. LoRA reduces the number of trainable parameters, but it does not di…
cs.AI2026
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Baha Rababah, Cuneyt Gurcan Akcora, Shahzeb Qamar +4
Post-Training Quantization has become widely used to compress large language models to make them deployable on resource-constrained devices. However, the evaluation of quantization…
cs.CR2024
SoK: Prompt Hacking of Large Language Models
Baha Rababah, Shang, Wu +3
The safety and robustness of large language models (LLMs) based applications remain critical challenges in artificial intelligence. Among the key threats to these applications are…