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Shane Arora

Shane Arora has 2 papers in our library, in 2023–2025, most of them on Question answering, Information retrieval & search; often with Eunsol Choi, Hung-Ting Chen, Fangyuan Xu.

2 papers in our library · 2023–2025

OpenAlex · h-index 4 · 9 works · 92 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 797 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7782

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

🔬Allen Institute for Artificial IntelligenceSeattle · United States2024–2025 · 6 paperscurrent🎓University of Massachusetts AmherstAmherst Center · United States2024–2025 · 2 paperscurrent🎓University of WashingtonSeattle · United States2024 · 2 papers🎓Yale UniversityNew Haven · United States2024 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Question answering · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CaLMQA: Exploring culturally specific long-form question answering across 23 languages (2025 · 27 citations)
    Most multilingual QA research relies on material translated from English and on short or multiple-choice answers.
  2. Understanding Retrieval Augmentation for Long-Form Question Answering (2023 · 24 citations)
    Retrieval augmentation is widely used to give language models fresh evidence for long-form answers. However, most analyses of how models actually use retrieved text have focused on factoid QA with short, easily scored answers. This paper examines that question for long-form question answering (LFQA) through two…

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. OLMo: Accelerating the Science of Language Models ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetDirk Groeneveld et al.2024 · 429 citations
  2. 2 OLMo 2 Furious ↗arXiv:2501.00656 · no summary yetTeam OLMo et al.2025 · 262 citations
  3. CaLMQA: Exploring culturally specific long-form question answering across 23 languagesAnnual Meeting of the Association for Computational Linguistics (ACL)Shane Arora, Marzena Karpinska, Hung-Ting Chen, et al.2025 · 27 citations
  4. Understanding Retrieval Augmentation for Long-Form Question AnsweringarXiv:2310.12150Hung-Ting Chen, Fangyuan Xu, Shane Arora, et al.2023 · 24 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 797 times (of the 149,439 papers whose bibliographies we read); 183 citing papers with a page here →.

Updated

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