Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings
Yu-Chen Wei · 2024
This study investigates the detection of machine-generated text using several semantic embedding techniques, a critical issue in the era of advanced language models.Different methodologies were examined: GloVe embeddings, N-gram embedding models, Sentence BERT, and a concatenated embedding approach, against a fine-tuned RoBERTa baseline.The research was conducted within the framework of SemEval-2024 Task 8, encompassing tasks for binary and multi-class classification of machine-generated text.