--- title: "Neel Guha · Stanford University — author profile and papers" description: "Neel Guha, Stanford University: 4 papers on Elamur Research (2021–2025), h-index 16 — affiliations, topics, co-authors and who cites them…" canonical: "https://research.elamur.ai/free/en/authors/neel-guha/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/neel-guha/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Neel Guha # Neel Guha Neel Guha has 4 papers in our library, in 2021–2025, most of them on [Legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Daniel E. Ho](https://md.research.elamur.ai/free/en/authors/daniel-e-ho/), [Lucia Zheng](https://md.research.elamur.ai/free/en/authors/lucia-zheng/), [Peter Henderson](https://md.research.elamur.ai/free/en/authors/peter-henderson/). 4 papers in our library · 2021–2025 [OpenAlex](https://openalex.org/A5068178240) · [ORCID](https://orcid.org/0009-0003-5120-1726) · [**h-index 16**](https://openalex.org/authors/A5068178240) · [59 works](https://openalex.org/works?filter=author.id:A5068178240) · [3,435 citations](https://openalex.org/works?filter=author.id:A5068178240&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/neel-guha/#papers) · [🔗 Cited 2,192 times in our library](https://md.research.elamur.ai/free/en/authors/neel-guha/cited-by/) · [5 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #4358](https://md.research.elamur.ai/free/en/paper-stars/page/44/#star-4358) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Neel+Guha) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Neel+Guha) · [dblp](https://dblp.org/search?q=Neel+Guha) · [ACL Anthology](https://aclanthology.org/search/?q=Neel+Guha) ## Affiliations [🎓**Stanford University** Stanford · United States 2015–2026 · 26 papers*current*](https://md.research.elamur.ai/free/en/universities/stanford-university/) 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. 📚 Shelves 9 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Social Sciences · 3 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Political Science and International Relations · 3 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/political-science-and-international-relations/) · [Law · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/law/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) 🧭 Topics 5 Research subjects and languages the work is about.[Legal AI · 3 works](https://md.research.elamur.ai/free/en/topics/legal-ai/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/neel-guha/year/2025/) · [**2022** 2 papers](https://md.research.elamur.ai/free/en/authors/neel-guha/year/2022/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/authors/neel-guha/year/2021/) ## Papers The most cited first (OpenAlex and our library). 1. **[Ask Me Anything: A simple strategy for prompting language models](https://md.research.elamur.ai/free/en/papers/arora-2022-ask-me-anything-simple-strategy/)** *(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](https://md.research.elamur.ai/free/en/papers/zheng-2021-pretraining-help-assessing-self-supervised/)** *(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](https://md.research.elamur.ai/free/en/papers/henderson-2022-pile-law-learning-responsible-data/)** *(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](https://md.research.elamur.ai/free/en/papers/zheng-2025-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 ↗](https://arxiv.org/abs/2211.09110) arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yet**Percy Liang et al.*2022 · 1,001 citations* 2. [**Ask Me Anything: A simple strategy for prompting language models arXiv:2210.02441**Simran Arora, Avanika Narayan, Mayee F. Chen, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/arora-2022-ask-me-anything-simple-strategy/) 3. [**When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset arXiv:2104.08671**Lucia Zheng, Neel Guha, Brandon R. Anderson, et al.*2021 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2021-pretraining-help-assessing-self-supervised/) 4. **[Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://md.research.elamur.ai/free/en/papers/henderson-2022-pile-law-learning-responsible-data/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Peter Henderson, Mark S. Krass, Lucia Zheng, et al.*2022 · 133 citations* 5. [**A Reasoning-Focused Legal Retrieval Benchmark arXiv:2505.03970**Lucia Zheng, Neel Guha, Javokhir Arifov, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-reasoning-focused-legal-retrieval-benchmark/) 6. **[LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning ↗](https://arxiv.org/abs/2209.06120) arXiv:2209.06120 · no summary yet**Neel 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 ↗](https://openalex.org/W7167856760) bepress Legal Repository · no summary yet**Neel Guha et al.*2024 · 8 citations* 8. **[There is no free benchmark: An institutional view of legal AI benchmarking ↗](https://doi.org/10.1073/pnas.2509757122) Proceedings of the National Academy of Sciences · no summary yet**Neel Guha et al.*2026 · 1 citation* 9. **[Insights from law for building and evaluating AI systems ↗](https://doi.org/10.25740/dn179pk0699) Stanford Digital Repository · no summary yet**Neel Guha et al.*2026* ## Co-authors here 10 co-authors with a page, the most papers together first. - [Daniel E. Ho](https://md.research.elamur.ai/free/en/authors/daniel-e-ho/) ×3 - [Lucia Zheng](https://md.research.elamur.ai/free/en/authors/lucia-zheng/) ×3 - [Peter Henderson](https://md.research.elamur.ai/free/en/authors/peter-henderson/) ×3 - [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) ×2 - [Avanika Narayan](https://md.research.elamur.ai/free/en/authors/avanika-narayan/) - [Christopher Ré](https://md.research.elamur.ai/free/en/authors/christopher-re/) - [Dan Jurafsky](https://md.research.elamur.ai/free/en/authors/dan-jurafsky/) - [Frederic Sala](https://md.research.elamur.ai/free/en/authors/frederic-sala/) - [Ines Chami](https://md.research.elamur.ai/free/en/authors/ines-chami/) - [Simran Arora](https://md.research.elamur.ai/free/en/authors/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)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/): [NeurIPS 2022](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/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 →](https://md.research.elamur.ai/free/en/authors/neel-guha/cited-by/). Updated 2 November 2026