Tom B. Brown

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Scaling Laws for Autoregressive Generative Modeling pdf

Mark Chen, Alec Radford, Heewoo Jun, 2020, ArXiv.

Measuring the Algorithmic Efficiency of Neural Networks pdf

Tom B. Brown, Danny Hernandez, Danny Hernandez, 2020, ArXiv.

Fine-Tuning Language Models from Human Preferences pdf

Alec Radford, Dario Amodei, Tom B. Brown, 2019, ArXiv.

Adversarial Patch pdf

Martín Abadi, Aurko Roy, Tom B. Brown, 2017, ArXiv.

Skill Rating for Generative Models pdf

Ian J. Goodfellow, Augustus Odena, Tom B. Brown, 2018, ArXiv.

Is Generator Conditioning Causally Related to GAN Performance? pdf

Colin Raffel, Ian J. Goodfellow, Augustus Odena, 2018, ICML.

Language Models are Few-Shot Learners pdf

Mark Chen, Ilya Sutskever, Alec Radford, 2020, NeurIPS.

Scaling Laws for Neural Language Models pdf

Alec Radford, Tom Henighan, Jared Kaplan, 2020, ArXiv.

Unrestricted Adversarial Examples pdf

Nicholas Carlini, Ian J. Goodfellow, Tom B. Brown, 2018, ArXiv.

Deep Reinforcement Learning from Human Preferences

Shane Legg, Jan Leike, Dario Amodei, 2017, NIPS.

Extracting Training Data from Large Language Models pdf

Colin Raffel, Dawn Song, Eric Wallace, 2020, USENIX Security Symposium.

Scaling Laws and Interpretability of Learning from Repeated Data

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Language Models (Mostly) Know What They Know

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Ian J. Goodfellow, Tom B. Brown, Jonas Rauber, 2016, 1610.00768.

In-context Learning and Induction Heads

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Predictability and Surprise in Large Generative Models

Tom B. Brown, Dario Amodei, T. Henighan, 2022, FAccT.

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

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

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

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Measuring Progress on Scalable Oversight for Large Language Models

Tom B. Brown, Dario Amodei, Azalia Mirhoseini, 2022, ArXiv.

Discovering Language Model Behaviors with Model-Written Evaluations

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ACL.

Extracting Training Data from Large Language Models

Colin Raffel, Dawn Song, Eric Wallace, 2020, USENIX Security Symposium.

A General Language Assistant as a Laboratory for Alignment pdf

Dario Amodei, Tom Henighan, Tom B. Brown, 2021, ArXiv.

The Capacity for Moral Self-Correction in Large Language Models

Tom B. Brown, Dario Amodei, Azalia Mirhoseini, 2023, ArXiv.

Constitutional AI: Harmlessness from AI Feedback

Tom B. Brown, Dario Amodei, Azalia Mirhoseini, 2022, ArXiv.

Language Models (Mostly) Know What They Know

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Tom B. Brown, Dario Amodei, T. Henighan, 2022, ArXiv.

Fine-Tuning Language Models from Human Preferences

Tom B. Brown, Daniel M. Ziegler, Nisan Stiennon, 2019, ArXiv.

Active Learning is a Strong Baseline for Data Subset Selection

Tom B. Brown, Alec Radford, Prafulla Dhariwal, 2022 .

Testing Robustness Against Unforeseen Adversaries pdf

Yi Sun, Dan Hendrycks, Daniel Kang, 2019, ArXiv.

Transfer of Adversarial Robustness Between Perturbation Types pdf

Yi Sun, Tom Brown, Dan Hendrycks, 2019, ArXiv.

Testing Robustness Against Unforeseen Adversaries.

Tom B. Brown, Daniel Kang, J. Steinhardt, 2019 .

Extracting Training Data from Large Language Models

Tom B. Brown, Florian Tramèr, Nicholas Carlini, 2020, USENIX Security Symposium.

Predictability and Surprise in Large Generative Models

Tom B. Brown, Dario Amodei, T. Henighan, 2022, FAccT.

Language Models are Few-Shot Learners

Tom B. Brown, Daniel M. Ziegler, Jeff Wu, 2020, NeurIPS.