Sam McCandlish

According to our database1, Sam McCandlish authored at least 26 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2023
Specific versus General Principles for Constitutional AI.
CoRR, 2023

Towards Understanding Sycophancy in Language Models.
CoRR, 2023

Studying Large Language Model Generalization with Influence Functions.
CoRR, 2023

Measuring Faithfulness in Chain-of-Thought Reasoning.
CoRR, 2023

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning.
CoRR, 2023

Towards Measuring the Representation of Subjective Global Opinions in Language Models.
CoRR, 2023

The Capacity for Moral Self-Correction in Large Language Models.
CoRR, 2023


2022
Discovering Language Model Behaviors with Model-Written Evaluations.
CoRR, 2022

Constitutional AI: Harmlessness from AI Feedback.
CoRR, 2022

Measuring Progress on Scalable Oversight for Large Language Models.
CoRR, 2022

In-context Learning and Induction Heads.
CoRR, 2022

Toy Models of Superposition.
CoRR, 2022

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.
CoRR, 2022

Language Models (Mostly) Know What They Know.
CoRR, 2022

Scaling Laws and Interpretability of Learning from Repeated Data.
CoRR, 2022

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.
CoRR, 2022

Predictability and Surprise in Large Generative Models.
CoRR, 2022


2021
A General Language Assistant as a Laboratory for Alignment.
CoRR, 2021

Evaluating Large Language Models Trained on Code.
CoRR, 2021

Scaling Laws for Transfer.
CoRR, 2021

2020
Scaling Laws for Autoregressive Generative Modeling.
CoRR, 2020

Scaling Laws for Neural Language Models.
CoRR, 2020


2018
An Empirical Model of Large-Batch Training.
CoRR, 2018


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