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Scaling Laws for Autoregressive Generative Modeling
We identify empirical scaling laws for the cross-entropy loss in four do...
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Language Models are Few-Shot Learners
Recent work has demonstrated substantial gains on many NLP tasks and ben...
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Measuring the Algorithmic Efficiency of Neural Networks
Three factors drive the advance of AI: algorithmic innovation, data, and...
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Scaling Laws for Neural Language Models
We study empirical scaling laws for language model performance on the cr...
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Fine-Tuning Language Models from Human Preferences
Reward learning enables the application of reinforcement learning (RL) t...
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Unrestricted Adversarial Examples
We introduce a two-player contest for evaluating the safety and robustne...
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Is Generator Conditioning Causally Related to GAN Performance?
Recent work (Pennington et al, 2017) suggests that controlling the entir...
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Adversarial Patch
We present a method to create universal, robust, targeted adversarial im...
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Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact useful...
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