Explaining Debugging Strategies to End-User Programmers. - Neeraja Subrahmaniyan; Cory Kissinger; Kyle Rector; Derek Inman; Jared Kaplan; Laura Beckwith; Margaret M. Burnett (VL/HCC, 2007)

An Empirical Model of Large-Batch Training. - Sam McCandlish; Jared Kaplan; Dario Amodei; OpenAI Dota Team (CoRR, 2018)

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)

A Neural Scaling Law from the Dimension of the Data Manifold. - Utkarsh Sharma; Jared Kaplan (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)

Data and Parameter Scaling Laws for Neural Machine Translation. - Mitchell A. Gordon; Kevin Duh; Jared Kaplan (EMNLP (1), 2021)

Scaling Laws for Transfer. - Danny Hernandez; Jared Kaplan; Tom Henighan; Sam McCandlish (CoRR, 2021)

Explaining Neural Scaling Laws. - Yasaman Bahri; Ethan Dyer; Jared Kaplan; Jaehoon Lee 001; Utkarsh Sharma (CoRR, 2021)

Evaluating Large Language Models Trained on Code. - Mark Chen 003; Jerry Tworek; Heewoo Jun; Qiming Yuan; Henrique Pondé de Oliveira Pinto; Jared Kaplan; Harri Edwards; Yuri Burda; Nicholas Joseph; Greg Brockman; Alex Ray; Raul Puri; et al. (CoRR, 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)

Language Models (Mostly) Know What They Know. - Saurav Kadavath; Tom Conerly; Amanda Askell; Tom Henighan; Dawn Drain; Ethan Perez; Nicholas Schiefer; Zac Hatfield-Dodds; Nova DasSarma; Eli Tran-Johnson; Scott Johnston; Sheer El Showk; et al. (CoRR, 2022)

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. - Deep Ganguli; Liane Lovitt; Jackson Kernion; Amanda Askell; Yuntao Bai; Saurav Kadavath; Ben Mann; Ethan Perez; Nicholas Schiefer; Kamal Ndousse; Andy Jones; Sam Bowman 001; et al. (CoRR, 2022)

Toy Models of Superposition. - Nelson Elhage; Tristan Hume; Catherine Olsson; Nicholas Schiefer; Tom Henighan; Shauna Kravec; Zac Hatfield-Dodds; Robert Lasenby; Dawn Drain; Carol Chen; Roger B. Grosse; Sam McCandlish; et al. (CoRR, 2022)

In-context Learning and Induction Heads. - Catherine Olsson; Nelson Elhage; Neel Nanda; Nicholas Joseph; Nova DasSarma; Tom Henighan; Ben Mann; Amanda Askell; Yuntao Bai; Anna Chen; Tom Conerly; Dawn Drain; et al. (CoRR, 2022)

Measuring Progress on Scalable Oversight for Large Language Models. - Samuel R. Bowman; Jeeyoon Hyun; Ethan Perez; Edwin Chen; Craig Pettit; Scott Heiner; Kamile Lukosiute; Amanda Askell; Andy Jones; Anna Chen; Anna Goldie; Azalia Mirhoseini; et al. (CoRR, 2022)

Constitutional AI: Harmlessness from AI Feedback. - Yuntao Bai; Saurav Kadavath; Sandipan Kundu; Amanda Askell; Jackson Kernion; Andy Jones; Anna Chen; Anna Goldie; Azalia Mirhoseini; Cameron McKinnon; Carol Chen; Catherine Olsson; et al. (CoRR, 2022)

Discovering Language Model Behaviors with Model-Written Evaluations. - Ethan Perez; Sam Ringer; Kamile Lukosiute; Karina Nguyen; Edwin Chen; Scott Heiner; Craig Pettit; Catherine Olsson; Sandipan Kundu; Saurav Kadavath; Andy Jones; Anna Chen; et al. (CoRR, 2022)

Scaling Laws from the Data Manifold Dimension. - Utkarsh Sharma; Jared Kaplan (J. Mach. Learn. Res., 2022)

Discovering Language Model Behaviors with Model-Written Evaluations. - Ethan Perez; Sam Ringer; Kamile Lukosiute; Karina Nguyen; Edwin Chen; Scott Heiner; Craig Pettit; Catherine Olsson; Sandipan Kundu; Saurav Kadavath; Andy Jones; Anna Chen; et al. (ACL (Findings), 2023)

The Capacity for Moral Self-Correction in Large Language Models. - Deep Ganguli; Amanda Askell; Nicholas Schiefer; Thomas I. Liao; Kamile Lukosiute; Anna Chen; Anna Goldie; Azalia Mirhoseini; Catherine Olsson; Danny Hernandez; Dawn Drain; Dustin Li; et al. (CoRR, 2023)

Towards Measuring the Representation of Subjective Global Opinions in Language Models. - Esin Durmus; Karina Nyugen; Thomas I. Liao; Nicholas Schiefer; Amanda Askell; Anton Bakhtin; Carol Chen; Zac Hatfield-Dodds; Danny Hernandez; Nicholas Joseph; Liane Lovitt; Sam McCandlish; et al. (CoRR, 2023)

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning. - Ansh Radhakrishnan; Karina Nguyen; Anna Chen; Carol Chen; Carson Denison; Danny Hernandez; Esin Durmus; Evan Hubinger; Jackson Kernion; Kamile Lukosiute; Newton Cheng; Nicholas Joseph; et al. (CoRR, 2023)

Measuring Faithfulness in Chain-of-Thought Reasoning. - Tamera Lanham; Anna Chen; Ansh Radhakrishnan; Benoit Steiner; Carson Denison; Danny Hernandez; Dustin Li; Esin Durmus; Evan Hubinger; Jackson Kernion; Kamile Lukosiute; Karina Nguyen; et al. (CoRR, 2023)

Studying Large Language Model Generalization with Influence Functions. - Roger B. Grosse; Juhan Bae; Cem Anil; Nelson Elhage; Alex Tamkin; Amirhossein Tajdini; Benoit Steiner; Dustin Li; Esin Durmus; Ethan Perez; Evan Hubinger; Kamile Lukosiute; et al. (CoRR, 2023)

Specific versus General Principles for Constitutional AI. - Sandipan Kundu; Yuntao Bai; Saurav Kadavath; Amanda Askell; Andrew Callahan; Anna Chen; Anna Goldie; Avital Balwit; Azalia Mirhoseini; Brayden McLean; Catherine Olsson; Cassie Evraets; et al. (CoRR, 2023)

Evaluating and Mitigating Discrimination in Language Model Decisions. - Alex Tamkin; Amanda Askell; Liane Lovitt; Esin Durmus; Nicholas Joseph; Shauna Kravec; Karina Nguyen; Jared Kaplan; Deep Ganguli (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Many-shot Jailbreaking. - Cem Anil; Esin Durmus; Nina Panickssery; Mrinank Sharma; Joe Benton; Sandipan Kundu; Joshua Batson; Meg Tong; Jesse Mu; Daniel Ford; Francesco Mosconi; Rajashree Agrawal; et al. (NeurIPS, 2024)

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. - Evan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam S. Jermyn; Amanda Askell; et al. (CoRR, 2024)

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. - Carson Denison; Monte MacDiarmid; Fazl Barez; David Duvenaud; Shauna Kravec; Samuel Marks; Nicholas Schiefer; Ryan Soklaski; Alex Tamkin; Jared Kaplan; Buck Shlegeris; Samuel R. Bowman; et al. (CoRR, 2024)

Sabotage Evaluations for Frontier Models. - Joe Benton; Misha Wagner; Eric Christiansen; Cem Anil; Ethan Perez; Jai Srivastav; Esin Durmus; Deep Ganguli; Shauna Kravec; Buck Shlegeris; Jared Kaplan; Holden Karnofsky; et al. (CoRR, 2024)

Clio: Privacy-Preserving Insights into Real-World AI Use. - Alex Tamkin; Miles McCain; Kunal Handa; Esin Durmus; Liane Lovitt; Ankur Rathi; Saffron Huang; Alfred Mountfield; Jerry Hong; Stuart Ritchie; Michael Stern; Brian Clarke; et al. (CoRR, 2024)

Alignment faking in large language models. - Ryan Greenblatt; Carson Denison; Benjamin Wright; Fabien Roger; Monte MacDiarmid; Samuel Marks; Johannes Treutlein; Tim Belonax; Jack Chen; David Duvenaud; Akbir Khan; Julian Michael; et al. (CoRR, 2024)

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming. - Mrinank Sharma; Meg Tong; Jesse Mu; Jerry Wei; Jorrit Kruthoff; Scott Goodfriend; Euan Ong; Alwin Peng; Raj Agarwal; Cem Anil; Amanda Askell; Nathan Bailey; et al. (CoRR, 2025)

Forecasting Rare Language Model Behaviors. - Erik Jones; Meg Tong; Jesse Mu; Mohammed Mahfoud; Jan Leike; Roger B. Grosse; Jared Kaplan; William Fithian; Ethan Perez; Mrinank Sharma (CoRR, 2025)

Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations. - Kunal Handa; Alex Tamkin; Miles McCain; Saffron Huang; Esin Durmus; Sarah Heck; Jared Mueller; Jerry Hong; Stuart Ritchie; Tim Belonax; Kevin K. Troy; Dario Amodei; et al. (CoRR, 2025)

Reasoning Models Don't Always Say What They Think. - Yanda Chen; Joe Benton; Ansh Radhakrishnan; Jonathan Uesato; Carson Denison; John Schulman; Arushi Somani; Peter Hase; Misha Wagner; Fabien Roger; Vladimir Mikulik; Samuel R. Bowman; et al. (CoRR, 2025)