1 citations · 1 across the 4 of their papers we have counts for
6 papers
Zero-Order Sharpness-Aware Minimization
Yao Fu, Yihang Jin, Chunxia Zhang +3
Prompt learning has become a key method for adapting large language models to specific tasks with limited data. However, traditional gradient-based optimization methods for tuning…
MSCR: Exploring the Vulnerability of LLMs' Mathematical Reasoning Abilities Using Multi-Source Candidate Replacement
Zhishen Sun, Guang Dai, Haishan Ye
LLMs demonstrate performance comparable to human abilities in complex tasks such as mathematical reasoning, but their robustness in mathematical reasoning under minor input perturb…
Numerical Sensitivity and Robustness: Exploring the Flaws of Mathematical Reasoning in Large Language Models
Zhishen Sun, Guang Dai, Ivor Tsang +1
LLMs have made significant progress in the field of mathematical reasoning, but whether they have true the mathematical understanding ability is still controversial. To explore thi…
FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed
Sizhe Dang, Yangyang Guo, Yanjun Zhao +4
Fine-tuning large language models (LLMs) often faces GPU memory bottlenecks: the backward pass of first-order optimizers like Adam increases memory usage to more than 10 times the…
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Jing Xue, Zhishen Sun, Haishan Ye +4
Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to det…
Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection
Xiangyu Chang, Guang Dai, Hao Di +1
This report presents a real-world case study demonstrating how prompt injection can attack large language model platforms such as ChatGPT according to a proposed injection framewor…