Large language model content detectors: Effects of human editing and ChatGPT mimicking individual style—A preliminary study

The use of large language model (LLM), such as ChatGPT, in academic writing is increasing, but the extent to which LLM-generated content can evade detection remains unclear. This descriptive pilot study investigates whether LLM-generated abstracts, edited by humans or LLM trained to mimic a specific writing style, can escape LLM detectors. Using a previously published original article, ChatGPT-4 generated an abstract (Abstract 1). This abstract underwent three modifications: context-based human editing (Abstract 2), stylistic human editing (Abstract 3), and ChatGPT editing incorporating the author's writing style (Abstract 4). The genuine human-written abstract from the original article served as Abstract 5. Five freely available LLM detectors analyzed these abstracts, providing LLM-generated probability scores. The genuinely LLM-generated manuscript (Abstract 1) was judged as LLM-generated with 82%-100% (median: 100%) probability. The genuinely human-written manuscript (Abstract 5) was judged as human-written with the LLM-generated probability of 0%-13% (median: 0%). Human-edited abstracts (Abstracts 2 and 3) exhibited a decreasing LLM-generated probability 4%-71% (median: 64%) and 2%-65% (median: 61%), respectively, but varied widely among detectors. The LLM-mimicked abstract (Abstract 4) was classified as LLM-generated, with LLM-generated probability ranging 82%-100% (median: 100%). The results showed variations across different LLM detectors. Supplementary experiments demonstrated a similar trend. Human editing reduces LLM-detection probabilities but does not guarantee evasion. LLM-generated content mimicking a specific writing style remains largely detectable. This preliminary experiment provided a novel study concept. Further studies on various manuscripts and different LLM detection methods will enhance understanding of the relationship between LLM-aided paper writing and LLM detectors.

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Publication Details

Journal
Journal of Investigative Medicine
Published
2025-06-17
DOI
https://doi.org/10.1177/10815589251352572
Citations
4
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
9.92
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article

Large language model content detectors: Effects of human editing and ChatGPT mimicking individual style—A preliminary study

Shigeki Matsubara
4 citations
Journal of Investigative Medicine
Topic Modeling
9.92
article

Large language model content detectors: Effects of human editing and ChatGPT mimicking individual style—A preliminary study

Shigeki Matsubara
article en
4 citations

Abstract

The use of large language model (LLM), such as ChatGPT, in academic writing is increasing, but the extent to which LLM-generated content can evade detection remains unclear. This descriptive pilot study investigates whether LLM-generated abstracts, edited by humans or LLM trained to mimic a specific writing style, can escape LLM detectors. Using a previously published original article, ChatGPT-4 generated an abstract (Abstract 1). This abstract underwent three modifications: context-based human editing (Abstract 2), stylistic human editing (Abstract 3), and ChatGPT editing incorporating the author's writing style (Abstract 4). The genuine human-written abstract from the original article served as Abstract 5. Five freely available LLM detectors analyzed these abstracts, providing LLM-generated probability scores. The genuinely LLM-generated manuscript (Abstract 1) was judged as LLM-generated with 82%-100% (median: 100%) probability. The genuinely human-written manuscript (Abstract 5) was judged as human-written with the LLM-generated probability of 0%-13% (median: 0%). Human-edited abstracts (Abstracts 2 and 3) exhibited a decreasing LLM-generated probability 4%-71% (median: 64%) and 2%-65% (median: 61%), respectively, but varied widely among detectors. The LLM-mimicked abstract (Abstract 4) was classified as LLM-generated, with LLM-generated probability ranging 82%-100% (median: 100%). The results showed variations across different LLM detectors. Supplementary experiments demonstrated a similar trend. Human editing reduces LLM-detection probabilities but does not guarantee evasion. LLM-generated content mimicking a specific writing style remains largely detectable. This preliminary experiment provided a novel study concept. Further studies on various manuscripts and different LLM detection methods will enhance understanding of the relationship between LLM-aided paper writing and LLM detectors.

Journal of Investigative MedicineVol. 73(8)
Jichi Medical University (JP), Koga Hospital (JP), Tokyo Medical University Ibaraki Medical Center (JP)
Quality Education
Openalex Percentile: Top 2%
Topic Modeling
9.92
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