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Akhila Yerukola

Akhila Yerukola has 2 papers in our library, in 2019–2025, most of them on Language models & pretraining; often with Abigail See, Christopher D. Manning, Katharina Reinecke.

2 papers in our library · 2019–2025

OpenAlex · h-index 7 · 31 works · 393 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 342 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5899

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

🎓Carnegie Mellon UniversityPittsburgh · United States2022–2026 · 18 paperscurrent🔬Allen Institute for Artificial IntelligenceSeattle · United States2023–2026 · 4 paperscurrent🎓University of California, Los AngelesLos Angeles · United States2025–2026 · 2 paperscurrent🎓Johns Hopkins UniversityBaltimore · United States2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20251 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Do Massively Pretrained Language Models Make Better Storytellers? (2019 · 213 citations)
    The paper asks whether large-scale open-domain pretraining actually improves neural text generation, using story writing as the test case.
  2. NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models (2025 · 46 citations)
    Most cultural evaluations of LLMs ask models directly about norms, which shows what they know but not whether they can use that knowledge in a concrete user situation.

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. Do Massively Pretrained Language Models Make Better Storytellers?Conference on Computational Natural Language Learning (CoNLL)Abigail See, Aneesh Pappu, Rohun Saxena, et al.2019 · 213 citations
  2. NormAd: A Framework for Measuring the Cultural Adaptability of Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Abhinav Rao, Akhila Yerukola, Vishwa Shah, et al.2025 · 46 citations
  3. Don't Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetAkhila Yerukola et al.2023 · 9 citations
  4. Ambig-SWE: Interactive Agents to Overcome Underspecificity in Software Engineering ↗arXiv (Cornell University) · no summary yetSanidhya Vijayvargiya et al.2025 · 8 citations

 Co-authors here

5 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 342 times (of the 149,439 papers whose bibliographies we read); 75 citing papers with a page here →.

Updated

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