Joe Barrow

647 total citations · 1 hit paper
9 papers, 210 citations indexed

About

Joe Barrow is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Joe Barrow has authored 9 papers receiving a total of 210 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Sociology and Political Science. Recurrent topics in Joe Barrow's work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers) and Opinion Dynamics and Social Influence (1 paper). Joe Barrow is often cited by papers focused on Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers) and Opinion Dynamics and Social Influence (1 paper). Joe Barrow collaborates with scholars based in United States, China and Germany. Joe Barrow's co-authors include Isabel O. Gallegos, Tong Yu, Sungchul Kim, Ruiyi Zhang, Ryan A. Rossi, Nesreen K. Ahmed, Franck Dernoncourt, Md Mehrab Tanjim, Jordan Boyd‐Graber and John P. Lalor and has published in prestigious journals such as Computational Linguistics.

In The Last Decade

Joe Barrow

7 papers receiving 192 citations

Hit Papers

Bias and Fairness in Large Language Models: A Survey 2024 2026 2025 2024 40 80 120

Peers

Joe Barrow
Kawin Ethayarajh United States
Isabel O. Gallegos United States
Paul Röttger United Kingdom
Lizhou Fan United States
Shrimai Prabhumoye United States
Kaitlyn Zhou United States
Viet Dac Lai United States
Su Lin Blodgett United States
Pradyumna Tambwekar United States
Kawin Ethayarajh United States
Joe Barrow
Citations per year, relative to Joe Barrow Joe Barrow (= 1×) peers Kawin Ethayarajh

Countries citing papers authored by Joe Barrow

Since Specialization
Citations

This map shows the geographic impact of Joe Barrow's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joe Barrow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joe Barrow more than expected).

Fields of papers citing papers by Joe Barrow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joe Barrow. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joe Barrow. The network helps show where Joe Barrow may publish in the future.

Co-authorship network of co-authors of Joe Barrow

This figure shows the co-authorship network connecting the top 25 collaborators of Joe Barrow. A scholar is included among the top collaborators of Joe Barrow based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joe Barrow. Joe Barrow is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Gallegos, Isabel O., Ryan A. Rossi, Joe Barrow, et al.. (2025). Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes. 873–888.
2.
Saad-Falcon, Jon, Joe Barrow, Alexa Siu, et al.. (2024). PDFTriage: Question Answering over Long, Structured Documents. 153–169. 3 indexed citations
3.
Gallegos, Isabel O., Ryan A. Rossi, Joe Barrow, et al.. (2024). Bias and Fairness in Large Language Models: A Survey. Computational Linguistics. 50(3). 1097–1179. 141 indexed citations breakdown →
4.
Barrow, Joe, et al.. (2024). Chain of Logic: Rule-Based Reasoning with Large Language Models. 2721–2733. 2 indexed citations
5.
Barrow, Joe, Rajiv Jain, Nedim Lipka, et al.. (2021). Syntopical Graphs for Computational Argumentation Tasks. 1583–1595.
6.
Rodríguez, Pedro, et al.. (2021). Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?. 4486–4503. 27 indexed citations
7.
Barrow, Joe, Rajiv Jain, Vlad I. Morariu, et al.. (2020). A Joint Model for Document Segmentation and Segment Labeling. 313–322. 21 indexed citations
8.
Elgohary, Ahmed, et al.. (2020). It Takes Two to Lie: One to Lie, and One to Listen. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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