Search Results for author:
Found 10 papers, 5 papers with code
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).
Universal Jailbreak Backdoors from Poisoned Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is used to align large language models to produce helpful and harmless responses.
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
Despite efforts to align large language models to produce harmless responses, they are still vulnerable to jailbreak prompts that elicit unrestricted behaviour.
Personas as a Way to Model Truthfulness in Language Models
This allows the model to separate truth from falsehoods and controls the truthfulness of its generation.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
PassGPT: Password Modeling and (Guided) Generation with Large Language Models
Large language models (LLMs) successfully model natural language from vast amounts of text without the need for explicit supervision.
Red-Teaming the Stable Diffusion Safety Filter
We then reverse-engineer the filter and find that while it aims to prevent sexual content, it ignores violence, gore, and other similarly disturbing content.
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO
This work conducts the first analysis on the robustness against adversarial attacks on self-supervised Vision Transformers trained using DINO.
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
Adversarial attacks are a major challenge faced by current machine learning research.
Uneven Coverage of Natural Disasters in Wikipedia: the Case of Flood
We also note how coverage of floods in countries with the lowest income, as well as countries in South America, is substantially lower than the coverage of floods in middle-income countries.
