AuTexTification: Model Generated Text Attribution

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 of attributing model generated text to the model that generated it, out of six models.

Areg Mikael Sarvazyan, José Ángel González, Marc Franco-Salvador, Francisco Rangel, Berta Chulvi, Paolo Rosso (2023) Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains. Procesamiento del Lenguaje Natural, Revista nº 71, septiembre de 2023, pp. 275-288.
Abstract task
Ranking metric
Macro F1

Task results

System MacroF1
Drocks 0.6472
Drocks 0.6417
TALN-UPF 0.6145
iimasPLN 0.5143
UAEMex 0.3378
ANLP 0.1793

If you have published a result better than those on the list, send a message to indicating the result and the DOI of the article, along with a copy of it if it is not published openly.