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Rajesh Bhatt

Rajesh Bhatt has 2 papers in our library, in 2022–2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Brian Dillon, Farah Adeeba, Hassan Sajjad.

2 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 20 · 95 works · 3,090 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 190 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #8013

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 Massachusetts AmherstAmherst Center · United States1995–2026 · 42 paperscurrent🎓The University of Texas at AustinAustin · United States2002–2004 · 6 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SLING: Sino Linguistic Evaluation of Large Language Models (2022 · 41 citations)
    Measuring how much grammar pretrained Chinese language models actually know requires clean test data. The earlier CLiMP benchmark falls short because its vocabulary was translated from the English BLiMP and several of its templates produce pairs with no real acceptability contrast.
  2. UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu (2026 · 2 citations)
    The authors built a diagnostic benchmark that tests how well large language models have acquired Urdu grammar.

All works

8 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. A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu ↗Proceedings of the Third Linguistic Annotation Workshop (LAW III) · no summary yetRajesh Bhatt et al.2009 · 196 citations
  2. Expressives and Identity Conditions ↗Linguistic Inquiry · no summary yetChristopher E. Potts et al.2009 · 109 citations
  3. Towards a Multi-Representational Treebank ↗no summary yetFei Xia et al.2008 · 62 citations
  4. The Hindi/Urdu Treebank Project ↗Springer eBooks · no summary yetRiyaz Ahmad Bhat et al.2017 · 61 citations
  5. GEE! Grammar Error Explanation with Large Language Models ↗Findings of the Association for Computational Linguistics: NAACL 2024 · no summary yetYixiao Song et al.2024 · 43 citations
  6. SLING: Sino Linguistic Evaluation of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, et al.2022 · 41 citations
  7. Empty Categories in a Hindi Treebank ↗Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10) · no summary yetArchna Bhatia et al.2010 · 16 citations
  8. UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in UrduFindings of the Association for Computational LinguisticsFarah Adeeba, Brian Dillon, Hassan Sajjad, et al.2026 · 2 citations

 Co-authors here

6 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 190 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

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