Red Teaming Language Models with Language Models
An exploration of using language models to detect and prevent harmful behavior, providing insights and techniques for red teaming and mitigating risks in real-world applications.
Red Teaming Language Models with Language Models
Finding harmful model behaviors by automatically generating inputs using language models themselves.
Chrome 99: CSS Cascade Layers, a New Picker for Input Elements, and More
This technical blog post discusses the new features and updates in Chrome 99, including CSS cascade layers, a new picker for input elements, preparations for Chrome 100, origin trials, canvas 2D features, improvements in CSS calc(), and various deprecations and removals.
Solving (some) formal math olympiad problems
Exploring strategies for tackling formal math olympiad problems effectively
Announcing GPT-NeoX-20B
Introducing GPT-NeoX-20B, a collaborative 20 billion parameter model developed with CoreWeave.
How to Improve User Experience (and Behavior): Three Papers from Stanford's Alexa Prize Team
This blogpost discusses three papers from Stanford's Alexa Prize team on improving user experience and behavior. The papers cover topics such as understanding and predicting user dissatisfaction, handling offensive users, and increasing user initiative in socialbot conversations.
What Is New with Periskop in 2022
Updates and new features in the internal pull-based exception monitoring service Periskop in 2022.
Aligning language models to follow instructions
Enhancing language model accuracy through instruction alignment
Introducing text and code embeddings
Exploring the integration of text and code embeddings for enhanced data representation
DeepMind: The Podcast returns for Season 2
DeepMind: The Podcast returns for Season 2, exploring the latest breakthroughs, innovations, and challenges in AI.
DeepMind: The Podcast returns for Season 2
Exploring the potential and creation of artificial intelligence (AI) in Season 2 of the DeepMind podcast.
Text and code embeddings by contrastive pre-training
Exploring the power of text and code embeddings through contrastive pre-training