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Parag Singla

Parag Singla has 3 papers in our library, in 2021–2026, most of them on Language models & pretraining, Information extraction, Multilingual & cross-lingual NLP; often with Dinesh Garg, Dinesh Khandelwal, Mausam.

3 papers in our library · 2021–2026

OpenAlex · ORCID · h-index 20 · 134 works · 2,204 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 105 times in our library · 7 more in our library without a page yet · ⭐ Paper Stars ranking: #6023

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

🎓Indian Institute of Technology DelhiNew Delhi · India2011–2026 · 76 papersEmployment since 2011 · Professor, CSEcurrent🎓University of WashingtonSeattle · United States2005–2012 · 13 papers🎓The University of Texas at AustinAustin · United States2010–2011 · 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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workInformation extraction · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20262 papers20211 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. Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026)
    Distantly supervised relation extraction (DSRE) trains on noisy, bag-level labels obtained by aligning text with knowledge-base triples, yet it must make predictions for individual sentences.
  3. 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.

All works

10 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Explanations for CommonsenseQA: New Dataset and ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, et al.2021 · 164 citations
  2. Unifying Logical and Statistical AI ↗Proceedings - Symposium on Logic in Computer Science · no summary yetPedro Domingos et al.2016 · 92 citations
  3. Markov Logic ↗Lecture notes in computer science · no summary yetPedro Domingos et al.2008 · 39 citations
  4. Just Add Weights: Markov Logic for the Semantic Web ↗Lecture notes in computer science · no summary yetPedro Domingos et al.2008 · 29 citations
  5. Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition ↗Lecture notes in computer science · no summary yetPedro Domingos et al.2008 · 6 citations
  6. Markov Logic: A Language and Algorithms for Link Mining ↗Springer eBooks · no summary yetPedro Domingos et al.2010 · 4 citations
  7. Just add weights ↗no summary yetPedro Domingos et al.2008 · 1 citation
  8. Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation ExtractionAnnual Meeting of the Association for Computational Linguistics (ACL)Vipul Kumar Rathore, Malik Hammad Faisal, Parag Singla, et al.2026
  9. LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence CheckingFindings of the Association for Computational LinguisticsNeeva Oza, Ishaan Govil, Parul Gupta, et al.2026
  10. The alchemy system for statistical relational ai: User manual. BIBLIOGRAPHY ↗no summary yetStanley Kok et al.2010

 Co-authors here

3 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 105 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

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