37 citations · 148 across the 17 of their papers we have counts for
4 papers · 1 filter
ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search
Zeyu Shen, Basileal Imana, Tong Wu +3
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain vulnerable to attacks on the re…
Does More Inference-Time Compute Really Help Robustness?
Tong Wu, Chong Xiang, Jiachen T. Wang +4
Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-…
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Dennis Jacob, Chong Xiang, Prateek Mittal
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophical…
Effectively Controlling Reasoning Models through Thinking Intervention
Tong Wu, Chong Xiang, Jiachen T. Wang +2
Reasoning-enhanced large language models (LLMs) explicitly generate intermediate reasoning steps prior to generating final answers, helping the model excel in complex problem-solvi…