2 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
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…