17 citations · 36 across the 8 of their papers we have counts for
4 papers · 1 filter
DistillSeq: A Framework for Safety Alignment Testing in Large Language Models using Knowledge Distillation
Mingke Yang, Yuqi Chen, Yi Liu +1
Large Language Models (LLMs) have showcased their remarkable capabilities in diverse domains, encompassing natural language understanding, translation, and even code generation. Th…
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…
Generative AI to Generate Test Data Generators
Benoit Baudry, Khashayar Etemadi, Sen Fang +7
Generating fake data is an essential dimension of modern software testing, as demonstrated by the number and significance of data faking libraries. Yet, developers of faking librar…