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Rahul Nadkarni

Rahul Nadkarni has 2 papers in our library, in 2022–2026, most of them on Language models & pretraining; often with Noah A. Smith, Caiming Xiong, Chengzu Li.

2 papers in our library · 2022–2026

OpenAlex · h-index 3 · 8 works · 60 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 134 times in our library · ⭐ Paper Stars ranking: #5969

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

🎓University of WashingtonSeattle · United States2021–2026 · 4 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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Binding Language Models in Symbolic Languages (2022 · 170 citations)
    End-to-end neural systems perform well on NLP tasks but are hard to interpret and fragile. Symbolic programs are transparent but limited to what their grammar can express. Earlier neural-symbolic hybrids needed custom languages, task-specific modules and large training sets. The authors introduce BINDER, a framework…
  2. Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior (2026)
    Researchers often say that pretraining data shapes what a language model knows and how it is biased. Most of the evidence, however, is correlational and comes from finished models and whole corpora. Nadkarni and colleagues test this causally. Their recipe needs a model with intermediate checkpoints, optimizer states…

 Co-authors here

12 of 13 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 134 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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