70 citations · 82 across the 6 of their papers we have counts for
7 papers
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
Zhuowen Yuan, Zidi Xiong, Yi Zeng +4
Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains. However, the emergence of biases and the poten…
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
Mintong Kang, Nezihe Merve Gürel, Ning Yu +2
Despite the impressive capabilities of large language models (LLMs) across diverse applications, they still suffer from trustworthiness issues, such as hallucinations and misalignm…
GRATH: Gradual Self-Truthifying for Large Language Models
Weixin Chen, Dawn Song, Bo Li
Truthfulness is paramount for large language models (LLMs) as they are increasingly deployed in real-world applications. However, existing LLMs still struggle with generating truth…
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
Mintong Kang, Dawn Song, Bo Li
Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness. Recent studies show…
Effective and Efficient Federated Tree Learning on Hybrid Data
Qinbin Li, Chulin Xie, Xiaojun Xu +5
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, m…
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
Ruoxi Jia, David Dao, Boxin Wang +6
Given a data set containing millions of data points and a data consumer who is willing to pay for $ to train a machine learning (ML) model over , how…
Exploring the Space of Black-box Attacks on Deep Neural Networks
Arjun Nitin Bhagoji, Warren He, Bo Li +1
Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can "t…