7 citations · 8 across the 5 of their papers we have counts for
5 papers
Unraveling the Efficacy of Neuron Coverage Criteria on Testing Large Language Models
Xiaoning Ren, Jiayang Song, Chongyang Liu +3
Neuron coverage criteria aim to assess test suite quality and guide test input generation, but their effectiveness for Transformer-based language models remains unclear. We evaluat…
ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models
Zhuowen Yin, Chongyang Liu, Wenzhang Yang +2
Embodied Foundation Models (EFMs) integrate multimodal understanding, future-state reasoning, and executable robot actions. Yet their safety alignment for human-injury prevention r…
Improving Code Understanding in Large Language Models through Concept-Aware Consistency Learning
Xiaoning Ren, Qiang Hu, Wei Ma +6
Large language models (LLMs) have recently shown impressive results on diverse code-related tasks, benefiting from large-scale training and instruction tuning. However, studies rev…
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Jie Li, Yi Liu, Chongyang Liu +4
Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to…
A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
Jie Li, Yi Liu, Chongyang Liu +5
Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security…