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Aastha Jhunjhunwala

Aastha Jhunjhunwala has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Ayush Dattagupta, Chen Zhu, Dan Su.

2 papers in our library · 2024–2025

OpenAlex · h-index 5 · 17 works · 137 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 142 times in our library · ⭐ Paper Stars ranking: #9935

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

🎓National Institute of Technology DurgapurDurgapur · India2019–2022 · 3 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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksMaterials Science · 1 workMaterials Chemistry · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Nemotron-4 15B Technical Report (2024 · 30 citations)
    NVIDIA set out to build the strongest general-purpose LLM that fits on a single A100 or H100 GPU. Following the Chinchilla-inspired idea of spending compute on more data rather than more parameters, the authors trained a 15B-parameter decoder-only Transformer on 8 trillion tokens. The mix was about 70% English, 15%…
  2. OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery (2025 · 13 citations)
    General-purpose LLMs often lack the specialized vocabulary and multi-step reasoning that scientific problems require. Training a science model from scratch is very costly. The authors, from SES AI and NVIDIA, instead adapt LLaMA 3.1 70B in three stages:

 Co-authors here

12 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 142 times (of the 149,439 papers whose bibliographies we read); 55 citing papers with a page here →.

Updated

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