AI-Generated and Human-Written Text Detection Using DistilBERT

This research proposes a system for detecting AI-generated text, utilizing the DistilBERT model, a streamlined variant of the larger BERT architecture. The system is designed to address the growing challenge of differentiating between AI-generated and human-written text: classifying an essay or a paragraph as human-written or AI-generated. Text classification is the process of classifying documents into predefined categories based on their content The methodology encompasses comprehensive data preprocessing, tokenization and encoding, and strategic hyperparameter tuning. DistilBERT is fine-tuned on a curated dataset encompassing diverse text types to enhance its detection accuracy.

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Publication Details

Published
2024-09-30
DOI
https://doi.org/10.1109/smartblock4africa61928.2024.10779494
Citations
3
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.75
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article

AI-Generated and Human-Written Text Detection Using DistilBERT

Dominic Damoah, John Kingsley Arthur, Martin Doe, Joshua Mawudem Gakpetor et al.
3 citations
Handwritten Text Recognition Techniques
0.75
article

AI-Generated and Human-Written Text Detection Using DistilBERT

Dominic Damoah, John Kingsley Arthur, Martin Doe, Joshua Mawudem Gakpetor, Michael Yeboah-Sarpong Damoah, Michael Tetteh Asare
article en
3 citations

Abstract

This research proposes a system for detecting AI-generated text, utilizing the DistilBERT model, a streamlined variant of the larger BERT architecture. The system is designed to address the growing challenge of differentiating between AI-generated and human-written text: classifying an essay or a paragraph as human-written or AI-generated. Text classification is the process of classifying documents into predefined categories based on their content The methodology encompasses comprehensive data preprocessing, tokenization and encoding, and strategic hyperparameter tuning. DistilBERT is fine-tuned on a curated dataset encompassing diverse text types to enhance its detection accuracy.

Valley View University (GH)
Quality Education
Openalex Percentile: Top 26%
Handwritten Text Recognition Techniques
0.75
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