1 citations · 2 across the 5 of their papers we have counts for
5 papers
Fine-Tuning Jailbreaks under Highly Constrained Black-Box Settings: A Three-Pronged Approach
Xiangfang Li, Yu Wang, Bo Li
With the rapid advancement of large language models (LLMs), ensuring their safe use becomes increasingly critical. Fine-tuning is a widely used method for adapting models to downst…
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
Wenhan Dong, Zhen Sun, Yuemeng Zhao +9
Large language models (LLMs) have demonstrated potential in educational applications, yet their capacity to accurately assess the cognitive alignment of reading materials with stud…
Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
Kejia Chen, Jiawen Zhang, Jiacong Hu +4
Quantized large language models (LLMs) have gained increasing attention and significance for enabling deployment in resource-constrained environments. However, emerging studies on…
LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance
Yu Wang, Cailing Cai, Zhihua Xiao +1
Large language models (LLMs) are increasingly applied in fields such as finance, education, and governance due to their ability to generate human-like text and adapt to specialized…
EfficientLLM: Efficiency in Large Language Models
Zhengqing Yuan, Weixiang Sun, Yixin Liu +13
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…