🧑‍🔬 Neel Guha

🏛️ Affiliations

🎓Stanford UniversityStanford · United States2015–2026 · 26 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 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. Ask Me Anything: A simple strategy for prompting language models (2022 · 154 citations)
   Prompting large language models is fragile: minor wording changes can shift accuracy a lot, so practitioners often spend significant effort hand-tuning a single prompt per task.
2. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset (2021 · 134 citations)
   Earlier attempts to pretrain Transformers on legal text gave little or no improvement over general-purpose BERT.
3. Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset (2022 · 133 citations)
   Teams that pretrain large language models remove private or toxic text in inconsistent, improvised ways and usually ignore context.
4. A Reasoning-Focused Legal Retrieval Benchmark (2025 · 55 citations)
   Legal AI tools increasingly pair LLMs with retrievers over case law and statutes, but realistic English benchmarks for testing such pipelines are scarce.

All works

9 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Holistic Evaluation of Language Models ↗arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yetPercy Liang et al.2022 · 1,001 citations
2. Ask Me Anything: A simple strategy for prompting language modelsarXiv:2210.02441Simran Arora, Avanika Narayan, Mayee F. Chen, et al.2022 · 154 citations
3. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD DatasetarXiv:2104.08671Lucia Zheng, Neel Guha, Brandon R. Anderson, et al.2021 · 134 citations
4. Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal DatasetConference on Neural Information Processing Systems (NeurIPS)Peter Henderson, Mark S. Krass, Lucia Zheng, et al.2022 · 133 citations
5. A Reasoning-Focused Legal Retrieval BenchmarkarXiv:2505.03970Lucia Zheng, Neel Guha, Javokhir Arifov, et al.2025 · 55 citations
6. LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning ↗arXiv:2209.06120 · no summary yetNeel Guha et al.2022 · 27 citations
7. AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing ↗bepress Legal Repository · no summary yetNeel Guha et al.2024 · 8 citations
8. There is no free benchmark: An institutional view of legal AI benchmarking ↗Proceedings of the National Academy of Sciences · no summary yetNeel Guha et al.2026 · 1 citation
9. Insights from law for building and evaluating AI systems ↗Stanford Digital Repository · no summary yetNeel Guha et al.2026

🤝 Co-authors here

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

• Daniel E. Ho ×3
• Lucia Zheng ×3
• Peter Henderson ×3
• Christopher D. Manning ×2
• Avanika Narayan
• Christopher Ré
• Dan Jurafsky
• Frederic Sala
• Ines Chami
• Simran Arora

Venues

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

• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2022
• Proceedings of the National Academy of Sciences
• Stanford Digital Repository
• bepress Legal Repository

🔗 Cited by

Papers of our library cite their works 2,192 times (of the 149,439 papers whose bibliographies we read); 478 citing papers with a page here →.

Updated 2 November 2026

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