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Sumit Bhatia

Sumit Bhatia has 2 papers in our library, in 2014–2026, most of them on Speech act theory, Language models & pretraining, Summarization; often with Prasenjit Mitra.

2 papers in our library · 2014–2026

OpenAlex · ORCID · Wikidata · h-index 19 · 129 works · 1,415 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 18 times in our library · ⭐ Paper Stars ranking: #7575

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

🏢Adobe SystemsSan Jose · United States2021–2026 · 22 paperscurrent🔬IBM Research - IndiaNew Delhi · India2017–2021 · 22 papers🎓Pennsylvania State UniversityState College · United States2009–2013 · 15 papers🔬IBM Research - AlmadenSan Jose · United States2014–2016 · 10 papers🏢IBMArmonk · United States2014–2022 · 7 papers🏢IBMBengaluru · India2018–2020 · 4 papers🏢XeroxNorwalk · United States2013–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Dialogue systems · 1 workSpeech act theory · 1 workSummarization · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper

2010s

20141 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Summarizing Online Forum Discussions – Can Dialog Acts of Individual Messages Help? (2014 · 59 citations)
    Long threads on web forums often run across many pages, with several participants contributing competing opinions and fixes.
  2. Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck? (2026)
    Language models score well on BLiMP overall, yet on certain paradigms they stay below chance even after massive pre-training, and adding more tokens does not reliably fix this.

 Co-authors here

1 co-author 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 18 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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