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Dinesh Khandelwal

Dinesh Khandelwal has 2 papers in our library, in 2021–2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Dinesh Garg, Parag Singla.

2 papers in our library · 2021–2026

OpenAlex · h-index 8 · 22 works · 264 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 164 times in our library · ⭐ Paper Stars ranking: #6025

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

🏢IBMArmonk · United States2019–2026 · 9 paperscurrent🎓Indian Institute of Technology HyderabadSangāreddi · India2026 · 1 papercurrent🎓Indian Institute of Technology DelhiNew Delhi · India2016–2018 · 3 papers🏢IBMBengaluru · India2021 · 2 papers🔬IBM Research - IndiaNew Delhi · India2021 · 2 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.

 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 workComputer Science Applications · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Explanations for CommonsenseQA: New Dataset and Models (2021 · 164 citations)
    Most work on CommonsenseQA (CQA) aims only at picking the right answer. This paper instead asks how to explain a known gold answer, independent of any model. The authors define four desired qualities of a good explanation: comprehensive, refutation complete, minimal, and coherent. In practice, an explanation becomes…
  2. LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence Checking (2026)
    The paper asks whether large language models can reliably tell if two programs are functionally equivalent, meaning they return identical outputs for every input.

 Co-authors here

2 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 164 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

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