DIODE: A Dense Indoor and Outdoor DEpth Dataset. - Igor Vasiljevic; Nicholas I. Kolkin; Shanyi Zhang; Ruotian Luo; Haochen Wang; Falcon Z. Dai; Andrea F. Daniele; Mohammadreza Mostajabi; Steven Basart; Matthew R. Walter; Gregory Shakhnarovich (CoRR, 2019)

A Benchmark for Anomaly Segmentation. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (CoRR, 2019)

Natural Adversarial Examples. - Dan Hendrycks; Kevin Zhao; Steven Basart; Jacob Steinhardt; Dawn Song (CVPR, 2021)

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. - Dan Hendrycks; Steven Basart; Norman Mu; Saurav Kadavath; Frank Wang; Evan Dorundo; Rahul Desai; Tyler Zhu; Samyak Parajuli; Mike Guo; Dawn Song; Jacob Steinhardt; et al. (ICCV, 2021)

Aligning AI With Shared Human Values. - Dan Hendrycks; Collin Burns; Steven Basart; Andrew Critch; Jerry Li 001; Dawn Song; Jacob Steinhardt (ICLR, 2021)

Measuring Massive Multitask Language Understanding. - Dan Hendrycks; Collin Burns; Steven Basart; Andy Zou; Mantas Mazeika; Dawn Song; Jacob Steinhardt (ICLR, 2021)

Measuring Mathematical Problem Solving With the MATH Dataset. - Dan Hendrycks; Collin Burns; Saurav Kadavath; Akul Arora; Steven Basart; Eric Tang; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

Measuring Coding Challenge Competence With APPS. - Dan Hendrycks; Steven Basart; Saurav Kadavath; Mantas Mazeika; Akul Arora; Ethan Guo; Collin Burns; Samir Puranik; Horace He; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

Towards Robustness of Neural Networks. - Steven Basart (CoRR, 2021)

Scaling Out-of-Distribution Detection for Real-World Settings. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Andy Zou; Joseph Kwon; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (ICML, 2022)

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. - Mantas Mazeika; Eric Tang; Andy Zou; Steven Basart; Jun Shern Chan; Dawn Song; David A. Forsyth; Jacob Steinhardt; Dan Hendrycks (NeurIPS, 2022)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (ICML, 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Jonathan Ng; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (CoRR, 2023)

Representation Engineering: A Top-Down Approach to AI Transparency. - Andy Zou; Long Phan; Sarah Li Chen; James Campbell; Phillip Guo; Richard Ren; Alexander Pan; Xuwang Yin; Mantas Mazeika; Ann-Kathrin Dombrowski; Shashwat Goel; Nathaniel Li; et al. (CoRR, 2023)

The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning. - Nathaniel Li; Alexander Pan; Anjali Gopal; Summer Yue; Daniel Berrios; Alice Gatti; Justin D. Li; Ann-Kathrin Dombrowski; Shashwat Goel; Gabriel Mukobi; Nathan Helm-Burger; Rassin Lababidi; et al. (ICML, 2024)

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal. - Mantas Mazeika; Long Phan; Xuwang Yin; Andy Zou; Zifan Wang 001; Norman Mu; Elham Sakhaee; Nathaniel Li; Steven Basart; Bo Li 026; David A. Forsyth; Dan Hendrycks (ICML, 2024)

Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? - Richard Ren; Steven Basart; Adam Khoja; Alice Gatti; Long Phan; Xuwang Yin; Mantas Mazeika; Alexander Pan; Gabriel Mukobi; Ryan H. Kim; Stephen Fitz; Dan Hendrycks (NeurIPS, 2024)

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning. - Nathaniel Li; Alexander Pan; Anjali Gopal; Summer Yue; Daniel Berrios; Alice Gatti; Justin D. Li; Ann-Kathrin Dombrowski; Shashwat Goel; Long Phan; Gabriel Mukobi; Nathan Helm-Burger; et al. (CoRR, 2024)