Jared Kaplan | DeepAI

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Featured Co-authors

Samuel R. Bowman
79 publications
Roger Grosse
56 publications
Ilya Sutskever
53 publications
John Schulman
31 publications
Łukasz Kaiser
29 publications
Ethan Perez
28 publications
Jan Leike
27 publications
Dario Amodei
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Wojciech Zaremba
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Jaehoon Lee
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Stanislav Fort
25 publications

∙ 08/07/2023

Studying Large Language Model Generalization with Influence Functions

When trying to gain better visibility into a machine learning model in o...

0 Roger Grosse, et al. ∙

∙ 06/28/2023

Towards Measuring the Representation of Subjective Global Opinions in Language Models

Large language models (LLMs) may not equitably represent diverse global ...

0 Esin Durmus, et al. ∙

∙ 02/15/2023

The Capacity for Moral Self-Correction in Large Language Models

We test the hypothesis that language models trained with reinforcement l...

0 Deep Ganguli, et al. ∙

∙ 12/15/2022

Constitutional AI: Harmlessness from AI Feedback

As AI systems become more capable, we would like to enlist their help to...

0 Yuntao Bai, et al. ∙

∙ 11/04/2022

Measuring Progress on Scalable Oversight for Large Language Models

Developing safe and useful general-purpose AI systems will require us to...

0 Samuel R. Bowman, et al. ∙

∙ 09/24/2022

In-context Learning and Induction Heads

"Induction heads" are attention heads that implement a simple algorithm ...

8 Catherine Olsson, et al. ∙

∙ 09/21/2022

Toy Models of Superposition

Neural networks often pack many unrelated concepts into a single neuron ...

12 Nelson Elhage, et al. ∙

∙ 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...

0 Deep Ganguli, et al. ∙

∙ 07/11/2022

Language Models (Mostly) Know What They Know

We study whether language models can evaluate the validity of their own ...

12 Saurav Kadavath, et al. ∙

∙ 05/21/2022

Scaling Laws and Interpretability of Learning from Repeated Data

Recent large language models have been trained on vast datasets, but als...

0 Danny Hernandez, et al. ∙

∙ 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...

2 Yuntao Bai, et al. ∙

∙ 02/15/2022

Predictability and Surprise in Large Generative Models

Large-scale pre-training has recently emerged as a technique for creatin...

0 Deep Ganguli, et al. ∙

∙ 12/01/2021

A General Language Assistant as a Laboratory for Alignment

Given the broad capabilities of large language models, it should be poss...

11 Amanda Askell, et al. ∙

∙ 07/07/2021

Evaluating Large Language Models Trained on Code

We introduce Codex, a GPT language model fine-tuned on publicly availabl...

6 Mark Chen, et al. ∙

∙ 02/12/2021

Explaining Neural Scaling Laws

The test loss of well-trained neural networks often follows precise powe...

0 Yasaman Bahri, et al. ∙

∙ 02/02/2021

Scaling Laws for Transfer

We study empirical scaling laws for transfer learning between distributi...

0 Danny Hernandez, et al. ∙

∙ 10/28/2020

Scaling Laws for Autoregressive Generative Modeling

We identify empirical scaling laws for the cross-entropy loss in four do...

2 Tom Henighan, et al. ∙

∙ 05/28/2020

Language Models are Few-Shot Learners

Recent work has demonstrated substantial gains on many NLP tasks and ben...

34 Tom B. Brown, et al. ∙

∙ 04/22/2020

A Neural Scaling Law from the Dimension of the Data Manifold

When data is plentiful, the loss achieved by well-trained neural network...

0 Utkarsh Sharma, et al. ∙

∙ 01/23/2020

Scaling Laws for Neural Language Models

We study empirical scaling laws for language model performance on the cr...

0 Jared Kaplan, et al. ∙

∙ 12/14/2018

An Empirical Model of Large-Batch Training

In an increasing number of domains it has been demonstrated that deep le...

0 Sam McCandlish, et al. ∙

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