15 citations · 20 across the 4 of their papers we have counts for
5 papers
YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models
Junyu Lin, Meizhen Liu, Xiufeng Huang +12
As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained,…
RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments
Yuchuan Fu, Xiaohan Yuan, Dongxia Wang
The rapid deployment of Large language model (LLM) agents in critical domains like healthcare and finance necessitates robust security frameworks. To address the absence of standar…
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
Xuran Li, Jingyi Wang, Xiaohan Yuan +1
It is often desirable to remove (a.k.a. unlearn) a specific part of the training data from a trained neural network model. A typical application scenario is to protect the data hol…
S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models
Xiaohan Yuan, Jinfeng Li, Dongxia Wang +9
Generative large language models (LLMs) have revolutionized natural language processing with their transformative and emergent capabilities. However, recent evidence indicates that…
Prompting Frameworks for Large Language Models: A Survey
Xiaoxia Liu, Jingyi Wang, Jun Sun +5
Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing abou…