10 citations · 32 across the 14 of their papers we have counts for
14 papers
GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs
Haibo Jin, Ruoxi Chen, Peiyan Zhang +3
As Large Language Models (LLMs) become increasingly integral to various domains, their potential to generate harmful responses has prompted significant societal and regulatory conc…
Compositional Subspace Representation Fine-tuning for Adaptive Large Language Models
Andy Zhou
Adapting large language models to multiple tasks can cause cross-skill interference, where improvements for one skill degrade another. While methods such as LoRA impose orthogonali…
Tempest: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search
Andy Zhou, Ron Arel
We introduce Tempest, a multi-turn adversarial framework that models the gradual erosion of Large Language Model (LLM) safety through a tree search perspective. Unlike single-turn…
AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies
Yi Zeng, Yu Yang, Andy Zhou +9
Foundation models (FMs) provide societal benefits but also amplify risks. Governments, companies, and researchers have proposed regulatory frameworks, acceptable use policies, and…
Tamper-Resistant Safeguards for Open-Weight LLMs
Rishub Tamirisa, Bhrugu Bharathi, Long Phan +12
Rapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use. Open-weight LLMs present unique chal…
AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies
Yi Zeng, Kevin Klyman, Andy Zhou +6
We present a comprehensive AI risk taxonomy derived from eight government policies from the European Union, United States, and China and 16 company policies worldwide, making a sig…
Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters
Haibo Jin, Andy Zhou, Joe D. Menke +1
Large Language Models (LLMs) are typically harmless but remain vulnerable to carefully crafted prompts known as ``jailbreaks'', which can bypass protective measures and induce harm…
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Rishub Tamirisa, Chulin Xie, Wenxuan Bao +3
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via persona…
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Haibo Jin, Ruoxi Chen, Peiyan Zhang +2
The discovery of "jailbreaks" to bypass safety filters of Large Language Models (LLMs) and harmful responses have encouraged the community to implement safety measures. One major s…
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Andy Zhou, Bo Li, Haohan Wang
Despite advances in AI alignment, large language models (LLMs) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries can modify prompts to induce unwanted…
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Andy Zhou, Jindong Wang, Yu-Xiong Wang +1
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation. We…
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman +2
While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agen…
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Zeyi Huang, Andy Zhou, Zijian Lin +3
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain…
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
Rishub Tamirisa, John Won, Chengjun Lu +2
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local…