A Multi-Strategy Approach for AI-Generated Text Detection
This work addresses the challenge of identifying AI-generated content, which is important for ensuring authenticity in news and academic domains, but it is incremental as it builds on existing methods for a shared task.
The paper tackled the problem of detecting AI-generated text in news articles and academic abstracts by developing three systems, with the fine-tuned RoBERTa-base classifier achieving near-perfect results on development and test sets.
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.