39 citations · 131 across the 11 of their papers we have counts for
11 papers
A StrongREJECT for Empty Jailbreaks
Alexandra Souly, Qingyuan Lu, Dillon Bowen +8
Most jailbreak papers claim the jailbreaks they propose are highly effective, often boasting near-100% attack success rates. However, it is perhaps more common than not for jailbre…
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Sam Toyer, Olivia Watkins, Ethan Adrian Mendes +9
While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that su…
Learning to Model the World with Language
Jessy Lin, Yuqing Du, Olivia Watkins +4
To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents can learn to e…
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du +7
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…
Aligning Text-to-Image Models using Human Feedback
Kimin Lee, Hao Liu, Moonkyung Ryu +6
Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text…
Guiding Pretraining in Reinforcement Learning with Large Language Models
Yuqing Du, Olivia Watkins, Zihan Wang +5
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by…
Teachable Reinforcement Learning via Advice Distillation
Olivia Watkins, Trevor Darrell, Pieter Abbeel +2
Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitat…
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
Julius Frost, Olivia Watkins, Eric Weiner +4
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…
Auto-Tuned Sim-to-Real Transfer
Yuqing Du, Olivia Watkins, Trevor Darrell +2
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual pr…
Hierarchical Text Generation using an Outline
Mehdi Drissi, Olivia Watkins, Jugal Kalita
Many challenges in natural language processing require generating text, including language translation, dialogue generation, and speech recognition. For all of these problems, text…
Program Language Translation Using a Grammar-Driven Tree-to-Tree Model
Mehdi Drissi, Olivia Watkins, Aditya Khant +5
The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid s…