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Pratyush Maini

Pratyush Maini has 2 papers in our library, in 2024, most of them on Language models & pretraining; often with Avi Schwarzschild, David Grangier, J. Zico Kolter.

2 papers in our library · 2024

OpenAlex · h-index 7 · 32 works · 366 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 215 times in our library · ⭐ Paper Stars ranking: #6118

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢DatologyAIRedwood City · United States2025–2026 · 5 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2023–2025 · 10 papers🎓Indian Institute of Technology DelhiNew Delhi · India2020–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksSafety Research · 1 workSocial Sciences · 1 workArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. TOFU: A Task of Fictitious Unlearning for LLMs (2024 · 144 citations)
    LLMs can memorize and reveal private details about people. "Unlearning" means adjusting a trained model so it forgets particular data. However, it has been hard to tell whether existing unlearning methods produce a model that behaves like one that never saw that data. TOFU (Task of Fictitious Unlearning) addresses…
  2. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling (2024 · 90 citations)
    Pre-training large language models on raw web crawls is costly. Web text is noisy and badly written, scaling laws call for ever more compute and tokens, and the supply of high-quality text is limited. The authors introduce WRAP (Web Rephrase Augmented Pre-training). An off-the-shelf instruction-tuned model, such as…

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 215 times (of the 149,439 papers whose bibliographies we read); 58 citing papers with a page here →.

Updated

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