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        <title>Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?</title>
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        <namePart type="given">Wissam</namePart>
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        <namePart type="given">Virginie</namePart>
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        <name type="personal">
            <namePart type="given">Christophe</namePart>
            <namePart type="family">Servan</namePart>
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            <namePart type="given">Anne</namePart>
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    <abstract>Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) such as ChatGPT. This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common attack schemes. The proposed method involves translating an English dataset into French and training a classifier on the translated data. Results show that the detectors can effectively detect ChatGPT-generated text, with a degree of robustness against basic attack techniques in in-domain settings. However, vulnerabilities are evident in out-of-domain contexts, highlighting the challenge of detecting adversarial text. The study emphasizes caution when applying in-domain testing results to a wider variety of content. We provide our translated datasets and models as open-source resources.</abstract>
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%0 Conference Proceedings
%T Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?
%A Antoun, Wissam
%A Mouilleron, Virginie
%A Sagot, Benoît
%A Seddah, Djamé
%Y Servan, Christophe
%Y Vilnat, Anne
%S Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux – articles longs
%D 2023
%8 June
%I ATALA
%C Paris, France
%F antoun-etal-2023-towards
%X Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) such as ChatGPT. This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common attack schemes. The proposed method involves translating an English dataset into French and training a classifier on the translated data. Results show that the detectors can effectively detect ChatGPT-generated text, with a degree of robustness against basic attack techniques in in-domain settings. However, vulnerabilities are evident in out-of-domain contexts, highlighting the challenge of detecting adversarial text. The study emphasizes caution when applying in-domain testing results to a wider variety of content. We provide our translated datasets and models as open-source resources.
%U https://aclanthology.org/2023.jeptalnrecital-long.2
%P 14-27

Markdown (Informal)

[Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?](https://aclanthology.org/2023.jeptalnrecital-long.2) (Antoun et al., JEP/TALN/RECITAL 2023)

• Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect? (Antoun et al., JEP/TALN/RECITAL 2023)

ACL

• Wissam Antoun, Virginie Mouilleron, Benoît Sagot, and Djamé Seddah. 2023. Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?. In Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs, pages 14–27, Paris, France. ATALA.