81 citations · 108 across the 29 of their papers we have counts for
6 papers · 2 filters
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
Zheyang Xiong, Ziyang Cai, John Cooper +11
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform…
Revisiting Character-level Adversarial Attacks for Language Models
Elias Abad Rocamora, Yongtao Wu, Fanghui Liu +2
Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based met…
Robust NAS under adversarial training: benchmark, theory, and beyond
Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel +2
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence o…
Generalization of Scaled Deep ResNets in the Mean-Field Regime
Yihang Chen, Fanghui Liu, Yiping Lu +2
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate \…
Efficient local linearity regularization to overcome catastrophic overfitting
Elias Abad Rocamora, Fanghui Liu, Grigorios G. Chrysos +2
Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-ste…
Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks
Yixin Cheng, Markos Georgopoulos, Volkan Cevher +1
Large Language Models (LLMs) are susceptible to Jailbreaking attacks, which aim to extract harmful information by subtly modifying the attack query. As defense mechanisms evolve, d…