Testing of detection tools for AI-generated text

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00132774" target="_blank" >RIV/00216224:14330/23:00132774 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s40979-023-00146-z" target="_blank" >https://link.springer.com/article/10.1007/s40979-023-00146-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40979-023-00146-z" target="_blank" >10.1007/s40979-023-00146-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Testing of detection tools for AI-generated text

Popis výsledku v původním jazyce

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artifcial intelligence (AI) generated content in an academic environment and intensifed eforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifcally, the study seeks to answer research questions about whether existing detection tools can reliably diferentiate between human-written text and ChatGPTgenerated text, and whether machine translation and content obfuscation techniques afect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques signifcantly worsen the performance of tools. The study makes several signifcant contributions. First, it summarises up-to-date similar scientific and non-scientifc eforts in the feld. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

Název v anglickém jazyce

Testing of detection tools for AI-generated text

Popis výsledku anglicky

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artifcial intelligence (AI) generated content in an academic environment and intensifed eforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifcally, the study seeks to answer research questions about whether existing detection tools can reliably diferentiate between human-written text and ChatGPTgenerated text, and whether machine translation and content obfuscation techniques afect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques signifcantly worsen the performance of tools. The study makes several signifcant contributions. First, it summarises up-to-date similar scientific and non-scientifc eforts in the feld. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal for Educational Integrity

ISSN

1833-2595

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

26

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

39

Strana od-do

1-39

Kód UT WoS článku

001129231700001

EID výsledku v databázi Scopus

2-s2.0-85180443619