Solving Probability and Statistics Problems by Program Synthesis. - Leonard Tang; Elizabeth Ke; Nikhil Singh 003; Nakul Verma; Iddo Drori (CoRR, 2021)

A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More. - Iddo Drori; Sunny Tran; Roman Wang; Newman Cheng; Kevin Liu; Leonard Tang; Elizabeth Ke; Nikhil Singh 003; Taylor L. Patti; Jayson Lynch; Avi Shporer; Nakul Verma; et al. (CoRR, 2021)

Solving Probability and Statistics Problems by Probabilistic Program Synthesis at Human Level and Predicting Solvability. - Leonard Tang; Elizabeth Ke; Nikhil Singh 003; Bo Feng; Derek Austin; Nakul Verma; Iddo Drori (AIED (2), 2022)

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. - Dan Hendrycks; Andy Zou; Mantas Mazeika; Leonard Tang; Bo Li 026; Dawn Song; Jacob Steinhardt (CVPR, 2022)

LILA: A Unified Benchmark for Mathematical Reasoning. - Swaroop Mishra; Matthew Finlayson; Pan Lu; Leonard Tang; Sean Welleck; Chitta Baral; Tanmay Rajpurohit; Oyvind Tafjord; Ashish Sabharwal; Peter Clark; Ashwin Kalyan (EMNLP, 2022)

A Dataset and Benchmark for Automatically Answering and Generating Machine Learning Final Exams. - Sarah J. Zhang; Reece Shuttleworth; Derek Austin; Yann Hicke; Leonard Tang; Sathwik Karnik; Darnell Granberry; Iddo Drori (CoRR, 2022)

The Naughtyformer: A Transformer Understands Offensive Humor. - Leonard Tang; Alexander Cai; Steve Li; Jason Wang (CoRR, 2022)

The Naughtyformer: A Transformer Understands and Moderates Adult Humor (Student Abstract). - Leonard Tang; Alexander Cai; Jason Wang (AAAI, 2023)

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. - Steven H. Wang; Antoine Scardigli; Leonard Tang; Wei Chen; Dimitry Levkin; Anya Chen; Spencer Ball; Thomas Woodside; Oliver Zhang; Dan Hendrycks (EMNLP, 2023)

From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams. - Iddo Drori; Sarah J. Zhang; Reece Shuttleworth; Sarah Zhang; Keith Tyser; Zad Chin; Pedro Lantigua; Saisamrit Surbehera; Gregory Hunter; Derek Austin; Leonard Tang; Yann Hicke; et al. (KDD, 2023)

Degraded Polygons Raise Fundamental Questions of Neural Network Perception. - Leonard Tang; Dan Ley (NeurIPS, 2023)

Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation. - Leonard Tang; Tom Shlomi; Alexander Cai (CoRR, 2023)

Baselines for Identifying Watermarked Large Language Models. - Leonard Tang; Gavin Uberti; Tom Shlomi (CoRR, 2023)

Consistent Explanations in the Face of Model Indeterminacy via Ensembling. - Dan Ley; Leonard Tang; Matthew Nazari; Hongjin Lin; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

Endless Jailbreaks with Bijection Learning. - Brian R. Y. Huang; Maximilian Li; Leonard Tang (ICLR, 2025)

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)

Verdict: A Library for Scaling Judge-Time Compute. - Nimit Kalra; Leonard Tang (CoRR, 2025)