# A Multi-Strategy Approach for AI-Generated Text Detection

## TL;DR

This paper introduces three different AI-generated text detection systems, including a fine-tuned RoBERTa classifier, a classical TF-IDF + SVM, and an innovative ensemble model, with RoBERTa performing best.

## Contribution

The paper presents a multi-strategy approach combining traditional and novel ensemble techniques for improved AI-generated text detection.

## Key findings

- RoBERTa classifier achieved near-perfect detection accuracy.
- The ensemble model Candace leverages multiple Llama-3.2 models.
- Classical TF-IDF + SVM system provides a competitive baseline.

## 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.