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Identifying artificial intelligence-generated content using the DistilBERT transformer and NLP techniques.

Hikmat Ullah Khan1, Anam Naz2, Fawaz Khaled Alarfaj3

  • 1Department of Information Technology, University of Sargodha, Punjab, Pakistan. dr.hikmat.niazi@gmail.com.

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|July 2, 2025
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Summary
This summary is machine-generated.

This study introduces a DistilBERT model to identify artificial intelligence-generated content (AIGC), achieving 98% accuracy. This advancement is crucial for ensuring digital content authenticity and combating misinformation in the era of large language models (LLMs).

Keywords:
AI generationAcademicsArtificial intelligenceContent verificationDeep learningNatural language processingText classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The proliferation of artificial intelligence-generated content (AIGC) due to large language models (LLMs) presents significant challenges in verifying content authenticity.
  • Misinformation and plagiarism risks are escalating, necessitating robust methods for identifying AIGC in academic and professional contexts.
  • Current research actively seeks reliable techniques for AIGC detection to uphold digital content integrity.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate identification of artificial intelligence-generated text.
  • To explore the efficacy of transformer-based architectures, specifically DistilBERT, in capturing linguistic patterns indicative of AIGC.
  • To compare the performance of the proposed model against traditional machine learning and deep learning approaches using various textual features and word embeddings.

Main Methods:

  • Utilized a DistilBERT transformer, a lightweight variant of BERT, leveraging self-attention mechanisms for contextual relevance analysis.
  • Integrated deep learning models with word embeddings like GloVe and FastText.
  • Explored traditional machine learning techniques employing textual features for AIGC classification.

Main Results:

  • The DistilBERT-based model achieved a high predictive accuracy of 98% in identifying AIGC.
  • The proposed model significantly outperformed traditional deep learning models, such as LSTM with GloVe embeddings (93% accuracy).
  • Qualitative assessments confirmed the model's robust performance across diverse textual samples, demonstrating practical reliability.

Conclusions:

  • The DistilBERT-based approach offers a highly effective and accurate solution for detecting artificial intelligence-generated content.
  • This research contributes a reliable tool for maintaining content authenticity and combating the spread of AIGC-related misinformation.
  • The findings underscore the potential of advanced transformer architectures in addressing critical challenges posed by the rapid growth of AIGC.