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Shrinidhi Kumbhar

Shrinidhi Kumbhar has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Chitta Baral, Amir Saeidi, Divij Handa.

2 papers in our library · 2023–2024

OpenAlex · h-index 2 · 6 works · 18 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #8052

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

🎓Arizona State UniversityTempe · United States2023–2026 · 6 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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 4Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksEvaluation & benchmarks · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models (2023 · 27 citations)
    The paper asks how well language models handle logical reasoning expressed in natural language. Formal knowledge-representation systems needed heavy expert effort and did not scale. The authors first survey the field. They cover three reasoning types (deductive, inductive, abductive), the usual task formats, how…
  2. When "Competency" in Reasoning Opens the Door to Vulnerability: Jailbreaking LLMs via Novel Complex Ciphers (2024 · 15 citations)
    Safety training for LLMs usually covers harmful requests in natural language or in well-known encodings such as Base64. This work argues that this leaves a gap: stronger reasoning lets models decode ciphers invented on the spot, and defenders cannot anticipate such ciphers in alignment data.

 Co-authors here

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

 Cited by

Papers of our library cite their works 37 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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