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        <title>Human vs Machine: An Automated Machine-Generated Text Detection Approach</title>
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        <namePart type="given">Srijani</namePart>
        <namePart type="family">Debnath</namePart>
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        <namePart type="given">Sivaji</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <namePart type="given">Karunesh</namePart>
            <namePart type="family">Arora</namePart>
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    <abstract>With the advancement of natural language processing (NLP) and sophisticated Large Language Models (LLMs), distinguishing between human-written texts and machine-generated texts is quite difficult nowadays. This paper presents a systematic approach to classifying machine-generated text from human-written text with a combination of the transformer-based model and textual feature-based post-processing technique. We extracted five textual features: readability score, stop word score, spelling and grammatical error count, unique word score and human phrase count from both human-written and machine-generated texts separately and trained three machine learning models (SVM, Random Forest and XGBoost) with these scores. Along with exploring traditional machine-learning models, we explored the BiLSTM and transformer-based distilBERT models to enhance the classification performance. By training and evaluating with a large dataset containing both human-written and machine-generated text, our best-performing framework achieves an accuracy of 87.5%.</abstract>
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%0 Conference Proceedings
%T Human vs Machine: An Automated Machine-Generated Text Detection Approach
%A Jawaid, Urwah
%A Roy, Rudra
%A Pal, Pritam
%A Debnath, Srijani
%A Das, Dipankar
%A Bandyopadhyay, Sivaji
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F jawaid-etal-2024-human
%X With the advancement of natural language processing (NLP) and sophisticated Large Language Models (LLMs), distinguishing between human-written texts and machine-generated texts is quite difficult nowadays. This paper presents a systematic approach to classifying machine-generated text from human-written text with a combination of the transformer-based model and textual feature-based post-processing technique. We extracted five textual features: readability score, stop word score, spelling and grammatical error count, unique word score and human phrase count from both human-written and machine-generated texts separately and trained three machine learning models (SVM, Random Forest and XGBoost) with these scores. Along with exploring traditional machine-learning models, we explored the BiLSTM and transformer-based distilBERT models to enhance the classification performance. By training and evaluating with a large dataset containing both human-written and machine-generated text, our best-performing framework achieves an accuracy of 87.5%.
%U https://aclanthology.org/2024.icon-1.24/
%P 215-223

Markdown (Informal)

[Human vs Machine: An Automated Machine-Generated Text Detection Approach](https://aclanthology.org/2024.icon-1.24/) (Jawaid et al., ICON 2024)

• Human vs Machine: An Automated Machine-Generated Text Detection Approach (Jawaid et al., ICON 2024)

ACL

• Urwah Jawaid, Rudra Roy, Pritam Pal, Srijani Debnath, Dipankar Das, and Sivaji Bandyopadhyay. 2024. Human vs Machine: An Automated Machine-Generated Text Detection Approach. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 215–223, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).