4 citations · 7 across the 16 of their papers we have counts for
6 papers · 1 filter
MILE: A Mutation Testing Framework of In-Context Learning Systems
Zeming Wei, Yihao Zhang, Meng Sun
In-context Learning (ICL) has achieved notable success in the applications of large language models (LLMs). By adding only a few input-output pairs that demonstrate a new task, the…
Automata Extraction from Transformers
Yihao Zhang, Zeming Wei, Meng Sun
In modern machine (ML) learning systems, Transformer-based architectures have achieved milestone success across a broad spectrum of tasks, yet understanding their operational mecha…
Boosting Jailbreak Attack with Momentum
Yihao Zhang, Zeming Wei
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-known jailbreak attack. In p…
Exploring the Robustness of In-Context Learning with Noisy Labels
Chen Cheng, Xinzhi Yu, Haodong Wen +4
Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research inte…
Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models
Yihao Zhang, Zeming Wei, Jun Sun +1
Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent iss…
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Yihao Zhang, Hangzhou He, Jingyu Zhu +3
Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, y…