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02/15/2023
The Capacity for Moral Self-Correction in Large Language Models
We test the hypothesis that language models trained with reinforcement l...
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12/15/2022
Constitutional AI: Harmlessness from AI Feedback
As AI systems become more capable, we would like to enlist their help to...
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11/04/2022
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to...
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09/24/2022
In-context Learning and Induction Heads
"Induction heads" are attention heads that implement a simple algorithm ...
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09/21/2022
Toy Models of Superposition
Neural networks often pack many unrelated concepts into a single neuron ...
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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...
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07/11/2022
Language Models (Mostly) Know What They Know
We study whether language models can evaluate the validity of their own ...
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05/21/2022
Scaling Laws and Interpretability of Learning from Repeated Data
Recent large language models have been trained on vast datasets, but als...
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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...
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02/15/2022
Predictability and Surprise in Large Generative Models
Large-scale pre-training has recently emerged as a technique for creatin...
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12/01/2021
A General Language Assistant as a Laboratory for Alignment
Given the broad capabilities of large language models, it should be poss...
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02/02/2021
Scaling Laws for Transfer
We study empirical scaling laws for transfer learning between distributi...
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10/28/2020
Scaling Laws for Autoregressive Generative Modeling
We identify empirical scaling laws for the cross-entropy loss in four do...
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05/28/2020
Language Models are Few-Shot Learners
Recent work has demonstrated substantial gains on many NLP tasks and ben...
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01/23/2020
