7 papers
Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS
Pengfei He, Yue Xing, Juanhui Li +7
TThis paper argues that \textbf{a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems (LLM-MAS)}. These system…
Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention
Jie Ren, Yaxin Li, Shenglai Zeng +4
Recent advancements in text-to-image diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts. However, increasing resear…
Red-Teaming LLM Multi-Agent Systems via Communication Attacks
Pengfei He, Yupin Lin, Shen Dong +3
Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based…
Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study
Pengfei He, Yingqian Cui, Han Xu +4
In-context learning (ICL) has emerged as a powerful capability for large language models (LLMs) to adapt to downstream tasks by leveraging a few (demonstration) examples. Despite i…
Data Poisoning for In-context Learning
Pengfei He, Han Xu, Yue Xing +3
In the domain of large language models (LLMs), in-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks, relying on examples rather than retra…
Multi-Faceted Studies on Data Poisoning can Advance LLM Development
Pengfei He, Yue Xing, Han Xu +2
The lifecycle of large language models (LLMs) is far more complex than that of traditional machine learning models, involving multiple training stages, diverse data sources, and va…