John J. Nay

According to our database, John J. Nay authored at least 14 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John J. Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael A. Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li

CoRR, 2023

ARB: Advanced Reasoning Benchmark for Large Language Models.

Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, Aran Komatsuzaki

CoRR, 2023

Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence.

John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao, Meghana Bhat, Raghav Jain, Aaron Travis Lee, Jonathan H. Choi, Jungo Kasai

CoRR, 2023

Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards.

John J. Nay

CoRR, 2023

Large Language Models as Corporate Lobbyists.

John J. Nay

CoRR, 2023

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models.

Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya K, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John J. Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael A. Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans.

John J. Nay

CoRR, 2022

2017

A Machine Learning Approach to Modeling Dynamic Decision-Making in Strategic Interactions and Prediction Markets.

John J. Nay

PhD thesis, 2017

2016

Predicting Human Cooperation.

John J. Nay, Yevgeniy Vorobeychik

CoRR, 2016

Predicting and Understanding Law-Making with Machine Learning.

John J. Nay

CoRR, 2016

Betting and belief: Prediction markets and attribution of climate change.

John J. Nay, Martin Van der Linden, Jonathan M. Gilligan

Proceedings of the Winter Simulation Conference, 2016

Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text.

John J. Nay

Proceedings of the First Workshop on NLP and Computational Social Science, 2016

2015

Data-driven dynamic decision models.

John J. Nay, Jonathan M. Gilligan

Proceedings of the 2015 Winter Simulation Conference, 2015

Participatory simulations of urban flooding for learning and decision support.

Jonathan M. Gilligan, Corey E. Brady, Janey V. Camp, John J. Nay, Pratim Sengupta

Proceedings of the 2015 Winter Simulation Conference, 2015