164 citations · 234 across the 12 of their papers we have counts for
5 papers · 1 filter
Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning
Alex Beutel, Kai Xiao, Johannes Heidecke +1
Automated red teaming can discover rare model failures and generate challenging examples that can be used for training or evaluation. However, a core challenge in automated red tea…
Rule Based Rewards for Language Model Safety
Tong Mu, Alec Helyar, Johannes Heidecke +7
Reinforcement learning based fine-tuning of large language models (LLMs) on human preferences has been shown to enhance both their capabilities and safety behavior. However, in cas…
GPT-4o System Card
OpenAI, :, Aaron Hurst +416
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…
First-Person Fairness in Chatbots
Tyna Eloundou, Alex Beutel, David G. Robinson +7
Evaluating chatbot fairness is crucial given their rapid proliferation, yet typical chatbot tasks (e.g., resume writing, entertainment) diverge from the institutional decision-maki…
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
Eric Wallace, Kai Xiao, Reimar Leike +3
Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompt…