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Joe Barrow

Joe Barrow has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Bias, fairness & safety; often with Franck Dernoncourt, Junda Wu, Ryan Aponte.

3 papers in our library · 2023–2025

OpenAlex · h-index 9 · 31 works · 966 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 539 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #2247

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

🏢Adobe SystemsSan Jose · United States2020–2025 · 9 papers🎓University of Maryland, College ParkCollege Park · United States2019–2021 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 workDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Bias and Fairness in Large Language Models: A Survey (2023 · 1,082 citations)
    Large language models learn from enormous, mostly uncurated web text. Along the way they absorb social biases and can even amplify them: stereotypes, toxic or exclusionary wording, and weak handling of some dialects. These harms fall hardest on groups that are already marginalized. This survey, accepted at…
  2. A Survey of Small Language Models (2024 · 31 citations)
    Large language models perform well but need heavy compute, memory and data, so they usually run on centralized, specialized hardware.
  3. From Selection to Generation: A Survey of LLM-based Active Learning (2025 · 22 citations)
    Active learning (AL) traditionally lowers labeling effort by picking the most useful unlabeled examples with metrics such as uncertainty or diversity.

All works

6 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. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
  2. AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models ↗arXiv:2310.15140 · no summary yetSicheng Zhu et al.2023 · 90 citations
  3. Personalization of Large Language Models: A Survey ↗arXiv (Cornell University) · no summary yetZhehao Zhang et al.2024 · 61 citations
  4. A Survey of Small Language ModelsarXiv:2410.20011Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.2024 · 31 citations
  5. From Selection to Generation: A Survey of LLM-based Active LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, et al.2025 · 22 citations
  6. Mitigating Noisy Inputs for Question Answering ↗Interspeech (USB) · no summary yetDenis Peskov et al.2019 · 19 citations

 Co-authors here

12 of 20 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 539 times (of the 149,439 papers whose bibliographies we read); 115 citing papers with a page here →.

Updated

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