@inproceedings{baradia-etal-2025-mirror,
title = "Mirror Minds : An Empirical Study on Detecting {LLM}-Generated Text via {LLM}s",
author = "Baradia, Josh and
Gupta, Shubham and
Kundu, Suman",
editor = "Alam, Firoj and
Nakov, Preslav and
Habash, Nizar and
Gurevych, Iryna and
Chowdhury, Shammur and
Shelmanov, Artem and
Wang, Yuxia and
Artemova, Ekaterina and
Kutlu, Mucahid and
Mikros, George",
booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2025.genaidetect-1.3/",
pages = "59--67",
abstract = "The use of large language models (LLMs) is inevitable in text generation. LLMs are intelligent and slowly replacing the search engines. LLMs became the de facto choice for conversation, knowledge extraction, and brain storming. This study focuses on a question: {\textquoteleft}Can we utilize the generative capabilities of LLMs to detect AI-generated content?' We present a methodology and empirical results on four publicly available data sets. The result shows, with 90{\%} accuracy it is possible to detect AI-generated content by a zero-shot detector utilizing multiple LLMs."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="baradia-etal-2025-mirror">
<titleInfo>
<title>Mirror Minds : An Empirical Study on Detecting LLM-Generated Text via LLMs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Josh</namePart>
<namePart type="family">Baradia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shubham</namePart>
<namePart type="family">Gupta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Suman</namePart>
<namePart type="family">Kundu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Firoj</namePart>
<namePart type="family">Alam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Preslav</namePart>
<namePart type="family">Nakov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nizar</namePart>
<namePart type="family">Habash</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iryna</namePart>
<namePart type="family">Gurevych</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shammur</namePart>
<namePart type="family">Chowdhury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Artem</namePart>
<namePart type="family">Shelmanov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuxia</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Artemova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mucahid</namePart>
<namePart type="family">Kutlu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">George</namePart>
<namePart type="family">Mikros</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Conference on Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The use of large language models (LLMs) is inevitable in text generation. LLMs are intelligent and slowly replacing the search engines. LLMs became the de facto choice for conversation, knowledge extraction, and brain storming. This study focuses on a question: ‘Can we utilize the generative capabilities of LLMs to detect AI-generated content?’ We present a methodology and empirical results on four publicly available data sets. The result shows, with 90% accuracy it is possible to detect AI-generated content by a zero-shot detector utilizing multiple LLMs.</abstract>
<identifier type="citekey">baradia-etal-2025-mirror</identifier>
<location>
<url>https://aclanthology.org/2025.genaidetect-1.3/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>59</start>
<end>67</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Mirror Minds : An Empirical Study on Detecting LLM-Generated Text via LLMs
%A Baradia, Josh
%A Gupta, Shubham
%A Kundu, Suman
%Y Alam, Firoj
%Y Nakov, Preslav
%Y Habash, Nizar
%Y Gurevych, Iryna
%Y Chowdhury, Shammur
%Y Shelmanov, Artem
%Y Wang, Yuxia
%Y Artemova, Ekaterina
%Y Kutlu, Mucahid
%Y Mikros, George
%S Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
%D 2025
%8 January
%I International Conference on Computational Linguistics
%C Abu Dhabi, UAE
%F baradia-etal-2025-mirror
%X The use of large language models (LLMs) is inevitable in text generation. LLMs are intelligent and slowly replacing the search engines. LLMs became the de facto choice for conversation, knowledge extraction, and brain storming. This study focuses on a question: ‘Can we utilize the generative capabilities of LLMs to detect AI-generated content?’ We present a methodology and empirical results on four publicly available data sets. The result shows, with 90% accuracy it is possible to detect AI-generated content by a zero-shot detector utilizing multiple LLMs.
%U https://aclanthology.org/2025.genaidetect-1.3/
%P 59-67
Markdown (Informal)
[Mirror Minds : An Empirical Study on Detecting LLM-Generated Text via LLMs](https://aclanthology.org/2025.genaidetect-1.3/) (Baradia et al., GenAIDetect 2025)
ACL