Sam McCandlish | DeepAI

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Featured Co-authors

Samuel R. Bowman
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∙ 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. ∙

∙ 02/02/2021

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. ∙

∙ 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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