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Raghav Jain

Raghav Jain has 2 papers in our library, in 2023, most of them on Language models & pretraining, Reasoning, Legal AI; often with Adam Jatowt, John J. Nay, Jungo Kasai.

2 papers in our library · 2023

OpenAlex · ORCID · Wikidata · h-index 7 · 33 works · 201 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 74 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8049

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 PatnaPatna · India2022–2025 · 25 paperscurrent

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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workPolitical Science and International Relations · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023 · 67 citations)
    Large language models have been tested on many reasoning skills, but no broad study had checked whether they grasp everyday facts about time, such as how long, how often, or in what order things happen.
  2. Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence (2023 · 49 citations)
    The paper asks how well large language models can apply law, using U.S. federal tax law as a testbed. Tax suits this purpose because its authority sits mainly in two fixed sources: the U.S. Code's Title 26 and the Treasury Regulations contained in the CFR. Many tax questions also have one verifiable answer that…

All works

3 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. Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Raghav Jain, Daivik Sojitra, Arkadeep Acharya, et al.2023 · 67 citations
  2. Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities EmergencearXiv:2306.07075John J. Nay, David Karamardian, Sarah B. Lawsky, et al.2023 · 49 citations
  3. Semantic Extractor-Paraphraser based Abstractive Summarization ↗Proceedings of the 17th International Conference on Natural Language Processing (ICON) · no summary yetAnubhav Jangra et al.2020 · 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 74 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/raghav-jain/