arxiv:2509.00623

A Multi-Strategy Approach for AI-Generated Text Detection

Published on Aug 30

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Abstract

Three systems, including a fine-tuned RoBERTa classifier, a TF-IDF + SVM classifier, and an ensemble model using Llama-3.2 models and a custom Transformer encoder, were developed for detecting AI-generated content in news articles and academic abstracts, with the RoBERTa-based system performing the best.

AI-generated summary

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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