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ChatGPT难题:人工撰写的科学手稿被人工智能文本检测工具误判为人工智能创作。

The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool.

作者信息

Rashidi Hooman H, Fennell Brandon D, Albahra Samer, Hu Bo, Gorbett Tom

机构信息

Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States.

PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.

出版信息

J Pathol Inform. 2023 Oct 17;14:100342. doi: 10.1016/j.jpi.2023.100342. eCollection 2023.


DOI:10.1016/j.jpi.2023.100342
PMID:38116171
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10727991/
Abstract

AI Chat Bots such as ChatGPT are revolutionizing our AI capabilities, especially in text generation, to help expedite many tasks, but they introduce new dilemmas. The detection of AI-generated text has become a subject of great debate considering the AI text detector's known and unexpected limitations. Thus far, much research in this area has focused on the detection of AI-generated text; however, the goal of this study was to evaluate the opposite scenario, an AI-text detection tool's ability to discriminate human-generated text. Thousands of abstracts from several of the most well-known scientific journals were used to test the predictive capabilities of these detection tools, assessing abstracts from 1980 to 2023. We found that the AI text detector erroneously identified up to 8% of the known real abstracts as AI-generated text. This further highlights the current limitations of such detection tools and argues for novel detectors or combined approaches that can address this shortcoming and minimize its unanticipated consequences as we navigate this new AI landscape.

摘要

诸如ChatGPT这样的人工智能聊天机器人正在彻底改变我们的人工智能能力,尤其是在文本生成方面,以帮助加快许多任务的完成,但它们也带来了新的困境。考虑到人工智能文本检测器已知的和意想不到的局限性,人工智能生成文本的检测已成为一个激烈辩论的话题。到目前为止,该领域的许多研究都集中在人工智能生成文本的检测上;然而,本研究的目的是评估相反的情况,即人工智能文本检测工具区分人类生成文本的能力。来自几本最著名科学期刊的数千篇摘要被用于测试这些检测工具的预测能力,评估1980年至2023年的摘要。我们发现,人工智能文本检测器错误地将高达8%的已知真实摘要识别为人工智能生成的文本。这进一步凸显了此类检测工具目前的局限性,并呼吁开发新的检测器或采用组合方法,以解决这一缺点,并在我们探索这一全新的人工智能领域时,将其意外后果降至最低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/526ebccde348/mmc1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/30b9d9dd302b/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/6e5331b9465d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/57a5bd346b9a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/7549ebc9f084/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/526ebccde348/mmc1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/30b9d9dd302b/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/6e5331b9465d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/57a5bd346b9a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/7549ebc9f084/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37fa/10727991/526ebccde348/mmc1.jpg

相似文献

[1]
The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool.

J Pathol Inform. 2023-10-17

[2]
Accurately detecting AI text when ChatGPT is told to write like a chemist.

Cell Rep Phys Sci. 2023-11

[3]
Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers.

NPJ Digit Med. 2023-4-26

[4]
Detecting generative artificial intelligence in scientific articles: Evasion techniques and implications for scientific integrity.

Orthop Traumatol Surg Res. 2023-12

[5]
Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study.

J Med Internet Res. 2023-12-25

[6]
Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened.

J Med Internet Res. 2023-5-31

[7]
Between human and AI: assessing the reliability of AI text detection tools.

Curr Med Res Opin. 2024-3

[8]
An Applied Statistics dataset for human vs AI-generated answer classification.

Data Brief. 2024-3-2

[9]
The Potential and Concerns of Using AI in Scientific Research: ChatGPT Performance Evaluation.

JMIR Med Educ. 2023-9-14

[10]
What is the rate of text generated by artificial intelligence over a year of publication in Orthopedics & Traumatology: Surgery & Research? Analysis of 425 articles before versus after the launch of ChatGPT in November 2022.

Orthop Traumatol Surg Res. 2023-12

引用本文的文献

[1]
Can we trust academic AI detective? Accuracy and limitations of AI-output detectors.

Acta Neurochir (Wien). 2025-8-7

[2]
Prevalence of Artificial Intelligence-Generated Text in Neurosurgical Publications: Implications for Academic Integrity and Ethical Authorship.

Cureus. 2025-2-16

[3]
Academic Integrity Within the Medical Curriculum in the Age of Generative Artificial Intelligence.

Health Sci Rep. 2025-2-19

[4]
Ethics of Writing Personal Statements and Letters of Recommendations with Large Language Models.

ATS Sch. 2024-8-21

[5]
Detection of ChatGPT fake science with the xFakeSci learning algorithm.

Sci Rep. 2024-7-14

[6]
Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023.

JCO Clin Cancer Inform. 2024-5

[7]
Applications of Large Language Models in Pathology.

Bioengineering (Basel). 2024-3-31

本文引用的文献

[1]
Common statistical concepts in the supervised Machine Learning arena.

Front Oncol. 2023-2-14

[2]
Can an artificial intelligence chatbot be the author of a scholarly article?

J Educ Eval Health Prof. 2023

[3]
ChatGPT is fun, but not an author.

Science. 2023-1-27

[4]
ChatGPT and Other Large Language Models Are Double-edged Swords.

Radiology. 2023-4

[5]
Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions.

J Thromb Haemost. 2023-4

[6]
'Arms race with automation': professors fret about AI-generated coursework.

Nature. 2023-1-24

[7]
ChatGPT listed as author on research papers: many scientists disapprove.

Nature. 2023-1

[8]
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods.

Acad Pathol. 2019-9-3

[9]
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J Am Med Inform Assoc. 2011

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