🧑‍🔬 John J. Nay

🏛️ Affiliations

🎓New York UniversityNew York · United States2016–2023 · 18 papers🎓Stanford UniversityStanford · United States2016–2025 · 15 papers🎓Vanderbilt UniversityNashville · United States2014–2018 · 13 papers🎓Harvard UniversityCambridge · United States2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. ARB: Advanced Reasoning Benchmark for Large Language Models (2023 · 66 citations)
   Many popular LLM benchmarks are saturating. Models like GPT-4 now score highly on them even though they still lack expert-level competence. ARB (Advanced Reasoning Benchmark) offers a harder test built from graduate-level and professional problems in mathematics, physics, chemistry, biology and law. The sources were…
2. Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence (2023 · 49 citations)
   The paper asks how well large language models can apply law, using U.S. federal tax law as a testbed. Tax suits this purpose because its authority sits mainly in two fixed sources: the U.S. Code's Title 26 and the Treasury Regulations contained in the CFR. Many tax questions also have one verifiable answer that…

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. ARB: Advanced Reasoning Benchmark for Large Language ModelsarXiv:2307.13692Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, et al.2023 · 66 citations
2. Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities EmergencearXiv:2306.07075John J. Nay, David Karamardian, Sarah B. Lawsky, et al.2023 · 49 citations
3. Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards ↗arXiv:2301.10095 · no summary yetJohn J. Nay2023 · 26 citations
4. Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text ↗Proceedings of the First Workshop on NLP and Computational Social Science · no summary yetNay, John J.2016 · 22 citations
5. Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans ↗arXiv:2209.13020 · no summary yetJohn J. Nay2022 · 8 citations
6. Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetAtharvan Dogra et al.2025 · 2 citations

🤝 Co-authors here

4 co-authors with a page, the most papers together first.

• Aran Komatsuzaki
• Daniel Paleka
• Jungo Kasai
• Sarah B. Lawsky

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
• Proceedings of the First Workshop on NLP and Computational Social Science

🔗 Cited by

Papers of our library cite their works 109 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated 2 November 2026

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