activity
20232026
most citedAssessing the Reliability of Large Language Model Knowledge

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety

Ting Ma, Xiufeng Huang, Benlei Cui +43

As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safet…

cs.CV2026

Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety

Shikai Qiu, Xiaowen Xu, Benlei Cui +55

General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversarial nature of content and AI s…

cs.CL2024★ 3 cited

Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors

Weixuan Wang, Jingyuan Yang, Wei Peng

Large language models (LLMs) have achieved remarkable performance across many tasks, yet aligning them with desired behaviors remains challenging. Activation intervention has emerg…

cs.CL2024

Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs

Weixuan Wang, Barry Haddow, Minghao Wu +2

Large language models (LLMs) have revolutionized the field of natural language processing (NLP), and recent studies have aimed to understand their underlying mechanisms. However, m…

cs.CL2023★ 3 cited

Assessing the Reliability of Large Language Model Knowledge

Weixuan Wang, Barry Haddow, Alexandra Birch +1

Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. LLMs are typically evaluated using accuracy, yet this…