AuTexTification: Model Generated Text Detection

This task aims to boost research on the detection of text generated automatically by text generation models. Participants must develop models that exploit clues about linguistic form and meaning to distinguish automatically generated text from human text. This subtask consists in distinguishing between human and generated text. It is framed as a binary classification task of human text (Hum) and MGT  (Gen), where text from three domains is included in the training set, and submissions are evaluated in two unseen ones.