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Nikunj Saunshi

Nikunj Saunshi has 3 papers in our library, in 2018–2025, most of them on Reasoning, Language models & pretraining, Sentiment & emotion; often with Nishanth Dikkala, Sanjiv Kumar, Sashank Jakkam Reddi.

3 papers in our library · 2018–2025

OpenAlex · h-index 10 · 29 works · 681 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 179 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5445

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

🏢GoogleMountain View · United States2023–2025 · 7 paperscurrent🎓Princeton UniversityPrinceton · United States2018–2024 · 16 papers

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksPsychology · 1 workSocial Psychology · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Sentiment & emotion · 1 workReasoning · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20231 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Large Self-Annotated Corpus for Sarcasm (2018 · 258 citations)
    Sarcasm is hard to detect because it is rare and even people often miss it. Earlier datasets were small or artificially balanced, were labelled by outside annotators, or were made of short tweets. The authors built SARC (Self-Annotated Reddit Corpus) from Reddit comments posted between January 2009 and April 2017.…
  2. Reasoning with Latent Thoughts: On the Power of Looped Transformers (2025 · 44 citations)
    The authors, from Google Research and TTIC, test whether reasoning requires a large number of parameters. Their hypothesis is that for many such tasks, what matters most is the model's depth. To test this, they use a "looped" transformer. In it, the same block of k layers with shared weights is applied to the input L…
  3. Reasoning in Large Language Models Through Symbolic Math Word Problems (2023 · 31 citations)
    The paper asks how to judge whether large language models actually reason when they solve math word problems, rather than only checking whether the final answer is correct.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. A Large Self-Annotated Corpus for SarcasmLanguage Resources and Evaluation Conference (LREC)Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli2018 · 258 citations
  2. A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors ↗Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetMikhail Khodak et al.2018 · 128 citations
  3. Reasoning with Latent Thoughts: On the Power of Looped TransformersarXiv:2502.17416Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, et al.2025 · 44 citations
  4. Reasoning in Large Language Models Through Symbolic Math Word ProblemsFindings of the Association for Computational LinguisticsVedant Gaur, Nikunj Saunshi2023 · 31 citations

 Co-authors here

4 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 179 times (of the 149,439 papers whose bibliographies we read); 74 citing papers with a page here →.

Updated

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