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Jeevana Priya Inala

Jeevana Priya Inala has 2 papers in our library, in 2024, most of them on Language models & pretraining, Code & programming languages, Interpretability; often with Jianfeng Gao, Armando Solar-Lezama, Chandan Singh.

2 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 11 · 34 works · 760 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 179 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6830

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

🏢MicrosoftRedmond · United States2022–2026 · 11 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2015–2021 · 11 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2021 · 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).Computer Science · 2 worksSoftware · 2 worksArtificial Intelligence · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Code & programming languages · 1 workInterpretability · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Rethinking Interpretability in the Era of Large Language Models (2024 · 87 citations)
    This position paper from Microsoft Research asks how interpretable machine learning should evolve now that large language models can describe patterns in plain language.
  2. Is Self-Repair a Silver Bullet for Code Generation? (2024 · 82 citations)
    The paper asks whether letting a language model debug and fix its own code is actually worth the extra compute, compared with drawing more independent samples.

All works

4 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. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
  2. Is Self-Repair a Silver Bullet for Code Generation?arXiv:2306.09896Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, et al.2024 · 82 citations
  3. Fault-Aware Neural Code Rankers ↗arXiv:2206.03865 · no summary yetJeevana Priya Inala et al.2022 · 39 citations
  4. Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions ↗arXiv:2205.14318 · no summary yetAnsong Ni et al.2022 · 39 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 179 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

Updated

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