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Nitish Joshi

Nitish Joshi has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Question answering, Reasoning; often with He He, Richard Yuanzhe Pang, Vishakh Padmakumar.

2 papers in our library · 2022–2023

OpenAlex · h-index 9 · 19 works · 298 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 174 times in our library · ⭐ Paper Stars ranking: #4450

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

🎓K J Somaiya Medical CollegeMumbai · India2025 · 1 papercurrent🎓New York UniversityNew York · United States2019–2024 · 17 papers🎓Indian Institute of Technology BombayMumbai · India2019–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The 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 work

🧭 Topics 3Research subjects and languages the work is about.Question answering · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20231 paperTesting the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples20221 paperQuALITY: Question Answering with Long Input Texts, Yes!

 Papers

The most cited first (OpenAlex and our library).

  1. QuALITY: Question Answering with Long Input Texts, Yes! (2022 · 162 citations)
    Most strong language understanding models can only handle a few hundred words at once, and current reading-comprehension benchmarks rarely require a system to understand a whole long document.
  2. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples (2023 · 82 citations)
    Chain-of-thought prompting lets large language models do some deductive reasoning. However, earlier tests mostly used modus ponens and drew test items from the same distribution as the few-shot demonstrations. This study asks whether LLMs reason deductively in a general sense, meaning whether they can produce proofs…

 Co-authors here

11 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 174 times (of the 141,840 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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