95 citations · 205 across the 24 of their papers we have counts for
5 papers · 1 filter
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
Rusheb Shah, Quentin Feuillade--Montixi, Soroush Pour +3
Despite efforts to align large language models to produce harmless responses, they are still vulnerable to jailbreak prompts that elicit unrestricted behaviour. In this work, we in…
Universal Jailbreak Backdoors from Poisoned Human Feedback
Javier Rando, Florian Tramèr
Reinforcement Learning from Human Feedback (RLHF) is used to align large language models to produce helpful and harmless responses. Yet, prior work showed these models can be jailb…
Personas as a Way to Model Truthfulness in Language Models
Nitish Joshi, Javier Rando, Abulhair Saparov +2
Large language models (LLMs) are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world. While unintuitive from a…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…
PassGPT: Password Modeling and (Guided) Generation with Large Language Models
Javier Rando, Fernando Perez-Cruz, Briland Hitaj
Large language models (LLMs) successfully model natural language from vast amounts of text without the need for explicit supervision. In this paper, we investigate the efficacy of…