Deep Reinforcement Learning from Human Preferences. - Paul F. Christiano; Jan Leike; Tom B. Brown; Miljan Martic; Shane Legg; Dario Amodei (NIPS, 2017)

Adversarial Patch. - Tom B. Brown; Dandelion Mané; Aurko Roy; Martín Abadi; Justin Gilmer (CoRR, 2017)

Is Generator Conditioning Causally Related to GAN Performance? - Augustus Odena; Jacob Buckman; Catherine Olsson; Tom B. Brown; Christopher Olah; Colin Raffel; Ian J. Goodfellow (ICML, 2018)

Skill Rating for Generative Models. - Catherine Olsson; Surya Bhupatiraju; Tom B. Brown; Augustus Odena; Ian J. Goodfellow (CoRR, 2018)

Unrestricted Adversarial Examples. - Tom B. Brown; Nicholas Carlini; Chiyuan Zhang; Catherine Olsson; Paul F. Christiano; Ian J. Goodfellow (CoRR, 2018)

Fine-Tuning Language Models from Human Preferences. - Daniel M. Ziegler; Nisan Stiennon; Jeffrey Wu 003; Tom B. Brown; Alec Radford; Dario Amodei; Paul F. Christiano; Geoffrey Irving (CoRR, 2019)

Language Models are Few-Shot Learners. - Tom B. Brown; Benjamin Mann; Nick Ryder; Melanie Subbiah; Jared Kaplan; Prafulla Dhariwal; Arvind Neelakantan; Pranav Shyam; Girish Sastry; Amanda Askell; Sandhini Agarwal; Ariel Herbert-Voss; et al. (NeurIPS, 2020)

Scaling Laws for Neural Language Models. - Jared Kaplan; Sam McCandlish; Tom Henighan; Tom B. Brown; Benjamin Chess; Rewon Child; Scott Gray; Alec Radford; Jeffrey Wu 003; Dario Amodei (CoRR, 2020)

Measuring the Algorithmic Efficiency of Neural Networks. - Danny Hernandez; Tom B. Brown (CoRR, 2020)

Scaling Laws for Autoregressive Generative Modeling. - Tom Henighan; Jared Kaplan; Mor Katz; Mark Chen 003; Christopher Hesse; Jacob Jackson; Heewoo Jun; Tom B. Brown; Prafulla Dhariwal; Scott Gray; Chris Hallacy; Benjamin Mann; et al. (CoRR, 2020)

Extracting Training Data from Large Language Models. - Nicholas Carlini; Florian Tramèr; Eric Wallace; Matthew Jagielski; Ariel Herbert-Voss; Katherine Lee; Adam Roberts; Tom B. Brown; Dawn Song; Úlfar Erlingsson; Alina Oprea; Colin Raffel (USENIX Security Symposium, 2021)

A General Language Assistant as a Laboratory for Alignment. - Amanda Askell; Yuntao Bai; Anna Chen; Dawn Drain; Deep Ganguli; Tom Henighan; Andy Jones; Nicholas Joseph; Benjamin Mann; Nova DasSarma; Nelson Elhage; Zac Hatfield-Dodds; et al. (CoRR, 2021)

Predictability and Surprise in Large Generative Models. - Deep Ganguli; Danny Hernandez; Liane Lovitt; Amanda Askell; Yuntao Bai; Anna Chen; Tom Conerly; Nova DasSarma; Dawn Drain; Nelson Elhage; Sheer El Showk; Stanislav Fort; et al. (FAccT, 2022)

Predictability and Surprise in Large Generative Models. - Deep Ganguli; Danny Hernandez; Liane Lovitt; Nova DasSarma; Tom Henighan; Andy Jones; Nicholas Joseph; Jackson Kernion; Benjamin Mann; Amanda Askell; Yuntao Bai; Anna Chen; et al. (CoRR, 2022)

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. - Yuntao Bai; Andy Jones; Kamal Ndousse; Amanda Askell; Anna Chen; Nova DasSarma; Dawn Drain; Stanislav Fort; Deep Ganguli; Tom Henighan; Nicholas Joseph; Saurav Kadavath; et al. (CoRR, 2022)

Scaling Laws and Interpretability of Learning from Repeated Data. - Danny Hernandez; Tom B. Brown; Tom Conerly; Nova DasSarma; Dawn Drain; Sheer El Showk; Nelson Elhage; Zac Hatfield-Dodds; Tom Henighan; Tristan Hume; Scott Johnston; Benjamin Mann; et al. (CoRR, 2022)