Does <scp>ChatGPT</scp> Ignore Article Retractions and Other Reliability Concerns?

ABSTRACT Large language models (LLMs) like ChatGPT seem to be increasingly used for information seeking and analysis, including to support academic literature reviews. To test whether the results might sometimes include retracted research, we identified 217 retracted or otherwise concerning academic studies with high altmetric scores and asked ChatGPT 4o‐mini to evaluate their quality 30 times each. Surprisingly, none of its 6510 reports mentioned that the articles were retracted or had relevant errors, and it gave 190 relatively high scores (world leading, internationally excellent, or close). The 27 articles with the lowest scores were mostly accused of being weak, although the topic (but not the article) was described as controversial in five cases (e.g., about hydroxychloroquine for COVID‐19). In a follow‐up investigation, 61 claims were extracted from retracted articles from the set, and ChatGPT 4o‐mini was asked 10 times whether each was true. It gave a definitive yes or a positive response two‐thirds of the time, including for at least one statement that had been shown to be false over a decade ago. The results therefore emphasise, from an academic knowledge perspective, the importance of verifying information from LLMs when using them for information seeking or analysis.

Authors

Institutions

Publication Details

Journal
Learned Publishing
Published
2025-08-04
DOI
https://doi.org/10.1002/leap.2018
Citations
5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
3.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does ChatGPT Ignore Article Retractions and Other Reliability Concerns?

Kim Holmberg, Mike A Thelwall, Irini Katsirea, Marianna Lehtisaari
5 citations
Learned Publishing
Artificial Intelligence in Healthcare and Education
3.27
article

Does ChatGPT Ignore Article Retractions and Other Reliability Concerns?

Kim Holmberg, Mike A Thelwall, Irini Katsirea, Marianna Lehtisaari
article en
5 citations

Abstract

ABSTRACT Large language models (LLMs) like ChatGPT seem to be increasingly used for information seeking and analysis, including to support academic literature reviews. To test whether the results might sometimes include retracted research, we identified 217 retracted or otherwise concerning academic studies with high altmetric scores and asked ChatGPT 4o‐mini to evaluate their quality 30 times each. Surprisingly, none of its 6510 reports mentioned that the articles were retracted or had relevant errors, and it gave 190 relatively high scores (world leading, internationally excellent, or close). The 27 articles with the lowest scores were mostly accused of being weak, although the topic (but not the article) was described as controversial in five cases (e.g., about hydroxychloroquine for COVID‐19). In a follow‐up investigation, 61 claims were extracted from retracted articles from the set, and ChatGPT 4o‐mini was asked 10 times whether each was true. It gave a definitive yes or a positive response two‐thirds of the time, including for at least one statement that had been shown to be false over a decade ago. The results therefore emphasise, from an academic knowledge perspective, the importance of verifying information from LLMs when using them for information seeking or analysis.

Learned PublishingVol. 38(4)
University of Turku (FI), Turku University of Applied Sciences (FI), University of Sheffield (GB)
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
3.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.