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Datasets of the AuTexTification shared task at IberLEF 2023. 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 dataset includes the training and test splits with labels for all the subtasks and languages. Additionally, each file includes the domain, the model and the prompt used to generate each sample. The model label mapping for subtask 2 is: {"A": "bloom-1b7", "B": "bloom-3b", "C": "bloom-7b1", "D": "babbage", "E": "curie", "F": "text-davinci-003"}
text classification, language models, machine-generated text
text classification, language models, machine-generated text
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
views | 431 | |
downloads | 64 |