research
∙
02/15/2023
The Capacity for Moral Self-Correction in Large Language Models
We test the hypothesis that language models trained with reinforcement l...
share
research
∙
12/15/2022
Constitutional AI: Harmlessness from AI Feedback
As AI systems become more capable, we would like to enlist their help to...
research
∙
11/04/2022
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to...
research
∙
09/24/2022
In-context Learning and Induction Heads
"Induction heads" are attention heads that implement a simple algorithm ...
research
∙
09/21/2022
Toy Models of Superposition
Neural networks often pack many unrelated concepts into a single neuron ...
research
∙
08/23/2022
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
We describe our early efforts to red team language models in order to si...
research
∙
07/11/2022
Language Models (Mostly) Know What They Know
We study whether language models can evaluate the validity of their own ...
research
∙
05/21/2022
Scaling Laws and Interpretability of Learning from Repeated Data
Recent large language models have been trained on vast datasets, but als...
research
∙
04/12/2022
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
We apply preference modeling and reinforcement learning from human feedb...
research
∙
02/15/2022
Predictability and Surprise in Large Generative Models
Large-scale pre-training has recently emerged as a technique for creatin...
research
∙
12/01/2021
A General Language Assistant as a Laboratory for Alignment
Given the broad capabilities of large language models, it should be poss...
research
∙
07/07/2021
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly availabl...
research
∙
10/28/2020
Scaling Laws for Autoregressive Generative Modeling
We identify empirical scaling laws for the cross-entropy loss in four do...
research
∙
09/02/2020
Learning to summarize from human feedback
As language models become more powerful, training and evaluation are inc...
research
∙
05/28/2020
Language Models are Few-Shot Learners
Recent work has demonstrated substantial gains on many NLP tasks and ben...
research
∙
01/23/2020
Scaling Laws for Neural Language Models
We study empirical scaling laws for language model performance on the cr...
research
∙
09/18/2019
Fine-Tuning Language Models from Human Preferences
Reward learning enables the application of reinforcement learning (RL) t...
research
∙
12/14/2018
An Empirical Model of Large-Batch Training
In an increasing number of domains it has been demonstrated that deep le...
research
∙
11/15/2018
Reward learning from human preferences and demonstrations in Atari
To solve complex real-world problems with reinforcement learning, we can...
research
∙
10/19/2018
Supervising strong learners by amplifying weak experts
Many real world learning tasks involve complex or hard-to-specify object...
research
∙
07/26/2018
Variational Option Discovery Algorithms
We explore methods for option discovery based on variational inference a...
research
∙
05/02/2018
AI safety via debate
To make AI systems broadly useful for challenging real-world tasks, we n...
research
∙
02/20/2018
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
This report surveys the landscape of potential security threats from mal...
research
∙
06/12/2017
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact useful...
research
∙
11/28/2016
Learning a Natural Language Interface with Neural Programmer
Learning a natural language interface for database tables is a challengi...
research
∙
06/21/2016
Concrete Problems in AI Safety
Rapid progress in machine learning and artificial intelligence (AI) has ...
research
∙
12/08/2015