A Multi-Strategy Approach for AI-Generated Text Detection

Ali Zain, Sareem Farooqui, Muhammad Rafi
8/30/2025
cs.CLcs.AI

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Abstract

This paper presents presents three distinct systems developed for the M-DAIGT shared task on detecting AI generated content in news articles and academic abstracts. The systems includes: (1) A fine-tuned RoBERTa-base classifier, (2) A classical TF-IDF + Support Vector Machine (SVM) classifier , and (3) An Innovative ensemble model named Candace, leveraging probabilistic features extracted from multiple Llama-3.2 models processed by a customTransformer encoder.The RoBERTa-based system emerged as the most performant, achieving near-perfect results on both development and test sets.

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Cite this paper

@article{zain2025multistrategy,
  title  = {A Multi-Strategy Approach for AI-Generated Text Detection},
  author = {Ali Zain and Sareem Farooqui and Muhammad Rafi},
  year   = {2025},
  eprint = {2509.00623},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.00623v1}
}

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