Downloads provided by UsageCounts
Test datasets for 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. It consists of two tasks, both for English and Spanish: 1) Generated or Human: determine whether the text has been automatically generated or not. 2) Model Attribution: determine what language model generated a text.
text classification, language models, generated text
text classification, language models, generated text
| selected citations These citations are derived from selected sources. 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 | 363 | |
| downloads | 93 |

Views provided by UsageCounts
Downloads provided by UsageCounts