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Kensen Shi

Kensen Shi has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Code & programming languages; often with Charles Sutton, Aakanksha Chowdhery, Abhishek Rao.

2 papers in our library · 2022–2023

OpenAlex · ORCID · Wikidata · h-index 9 · 21 works · 2,382 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,769 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #732

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 States2019–2025 · 11 paperscurrent🎓University of California, BerkeleyBerkeley · United States2022–2023 · 2 papers🎓Stanford UniversityStanford · United States2014–2018 · 2 papers🎓Texas A&M UniversityCollege Station · 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. 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 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workCode & programming languages · 1 work

💡 Concepts 3Key ideas and methods explained on our concept pages.Few-shot learning · 1 workLanguage model · 1 workLarge language models · 1 work

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PaLM: Scaling Language Modeling with Pathways (2022 · 4,753 citations)
    This paper asks whether making a dense Transformer language model much larger still improves few-shot learning, where a model solves a task from a short prompt and a few examples instead of being finetuned.
  2. Natural Language to Code Generation in Interactive Data Science Notebooks (2023 · 63 citations)
    Data scientists often work in Jupyter-style notebooks, where each new request to an AI coding assistant depends on earlier cells, their execution state, and previous requests.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. PaLM: Scaling Language Modeling with PathwaysarXiv:2204.02311Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.2022 · 4,753 citations
  2. Natural Language to Code Generation in Interactive Data Science NotebooksAnnual Meeting of the Association for Computational Linguistics (ACL)Pengcheng Yin, Wen-Ding Li, Kefan Xiao, et al.2023 · 63 citations
  3. NExT: Teaching Large Language Models to Reason about Code Execution ↗arXiv:2404.14662 · no summary yetAnsong Ni et al.2024 · 30 citations

 Co-authors here

12 of 18 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 2,769 times (of the 149,439 papers whose bibliographies we read); 731 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kensen-shi/