A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

no code implementations • 19 May 2023 • Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A. Mustafa

Considering the fast development of LLMs, this survey does not intend to be complete (although it includes 300 references), especially when it comes to the applications of LLMs in various domains, but rather a collection of organised literature reviews and discussions to support the quick understanding of the safety and trustworthiness issues from the perspective of V&V.

Model-Agnostic Reachability Analysis on Deep Neural Networks

no code implementations • 3 Apr 2023 • Chi Zhang, Wenjie Ruan, Fu Wang, Peipei Xu, Geyong Min, Xiaowei Huang

Verification plays an essential role in the formal analysis of safety-critical systems.

Towards Verifying the Geometric Robustness of Large-scale Neural Networks

1 code implementation • 29 Jan 2023 • Fu Wang, Peipei Xu, Wenjie Ruan, Xiaowei Huang

Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric transformation.

Reachability Analysis of Neural Network Control Systems

1 code implementation • 28 Jan 2023 • Chi Zhang, Wenjie Ruan, Peipei Xu

We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model.

Rolling Shutter Correction

Towards the Quantification of Safety Risks in Deep Neural Networks

1 code implementation • 13 Sep 2020 • Peipei Xu, Wenjie Ruan, Xiaowei Huang

In this paper, we define safety risks by requesting the alignment of the network's decision with human perception.