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Shantanu Acharya

Shantanu Acharya has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining; often with Alexander Panchenko, Arne Köhn, Boris Ginsburg.

2 papers in our library · 2019–2024

OpenAlex · ORCID · h-index 4 · 16 works · 100 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 468 times in our library · ⭐ Paper Stars ranking: #4580

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

🎓New York UniversityNew York · United States2021–2025 · 2 papersEducation 2021–2023 · Masters, Computer Science🏢NvidiaSanta Clara · United States2023Employment since 2023 · Research Scientist
🏛️Simbo.aiBengaluru · India2019–2021Employment 2019–2021 · Machine Learning Engineer
🎓Universität HamburgHamburg · Germany2018Employment 2018 · NLP Research Intern, Computational Linguistics

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 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RULER: What's the Real Context Size of Your Long-Context Language Models? (2024 · 400 citations)
    Long-context language models are usually checked with the needle-in-a-haystack (NIAH) test. That test only shows whether a model can find one planted fact, so it says little about deeper use of long inputs. The NVIDIA authors built RULER, a fully synthetic benchmark whose input length and difficulty can be set…
  2. Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings (2019 · 62 citations)
    Taxonomies built automatically from text often contain two kinds of errors: orphans, which are terms left with no connections, and outliers, which are terms attached to the wrong parent.

 Co-authors here

10 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 468 times (of the 149,439 papers whose bibliographies we read); 112 citing papers with a page here →.

Updated

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