This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (311) Chatbots and Conversational Agents (531) Scientometrics, Infometrics, and Altmetrics (94) Epublishing and Open Access (86) Artificial Intelligence (1371) Ontologies, Classifications, and Coding (239) Development and Evaluation of Research Methods, Instruments and Tools (481)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52935, first published .
Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study

Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study

Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study

Authors of this article:

Joseph Mugaanyi1 Author Orcid Image ;   Liuying Cai2 Author Orcid Image ;   Sumei Cheng2 Author Orcid Image ;   Caide Lu1 Author Orcid Image ;   Jing Huang1 Author Orcid Image
Article Authors Cited by (6) Tweetations (9) Metrics

Joseph Mugaanyi   1 * , MBBS, MD ;   Liuying Cai   2 * , MPhil ;   Sumei Cheng   2 , PhD ;   Caide Lu   1 , MD, PhD ;   Jing Huang   1 , MD, PhD

1 Department of Hepato-Pancreato-Biliary Surgery, Ningbo Medical Center Lihuili Hospital, Health Science Center, Ningbo University, Ningbo, China

2 Institute of Philosophy, Shanghai Academy of Social Sciences, Shanghai, China

*these authors contributed equally

Corresponding Author:

  • Jing Huang, MD, PhD
  • Department of Hepato-Pancreato-Biliary Surgery, Ningbo Medical Center Lihuili Hospital
  • Health Science Center
  • Ningbo University
  • No 1111 Jiangnan Road
  • Ningbo, 315000
  • China
  • Phone: 86 13819803591
  • Email: huangjingonline@163.com