Abstract:
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[EN] This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference. AuTexTification consists ...[+]
[EN] This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference. AuTexTification consists of two subtasks: for Subtask 1, participants had to determine whether a text is human-authored or has been generated by a large language model. For Subtask 2, participants had to attribute a machine-generated text to one of six different text generation models. Our AuTexTification 2023 dataset contains more than 160.000 texts across two languages (English and Spanish) and five domains (tweets, reviews, news, legal, and how-to articles). A total of 114 teams signed up to participate, of which 36 sent 175 runs, and 20 of them sent their working notes. In this overview, we present the AuTexTification dataset and task, the submitted participating systems, and the results.
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[ES] Este artículo presenta un resumen de la tarea AuTexTification como
parte del workshop IberLEF 2023 sobre el Iberian Languages Evaluation Forum, en
el marco de la conferencia SEPLN 2023. AuTexTification consta de dos ...[+]
[ES] Este artículo presenta un resumen de la tarea AuTexTification como
parte del workshop IberLEF 2023 sobre el Iberian Languages Evaluation Forum, en
el marco de la conferencia SEPLN 2023. AuTexTification consta de dos subtareas:
en la Subtarea 1, los participantes tuvieron que determinar si un texto fue escrito
por un humano o generado por un modelo de lenguaje masivo. Para la Subtarea 2,
los participantes debían atribuir un texto generado automáticamente a uno de seis
modelos de generación de texto diferentes. El conjunto de datos AuTexTification
contiene más de 160.000 textos en dos idiomas (inglés y español) y cinco dominios (tweets, reseñas, noticias, legislación y artículos instructivos). Un total de 114
equipos se inscribieron para participar, de los cuales 36 enviaron 175 resultados y 20 de ellos enviaron artículos. En este artículo, presentamos el conjunto de datos y la tarea AuTexTification, los sistemas enviados por los participantes y sus resultados.
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Thanks:
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The work from Symanto has been partially funded by the Pro<SUP>2</SUP>Haters - Proactive Profiling of Hate Speech Spreaders (CDTi IDI-20210776), the XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy ...[+]
The work from Symanto has been partially funded by the Pro<SUP>2</SUP>Haters - Proactive Profiling of Hate Speech Spreaders (CDTi IDI-20210776), the XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (MICIN PLEC2021-007681), the OBULEX - OBservatorio del Uso de Lenguage sEXista en la red (IVACE IMINOD/2022/106), and the ANDHI - ANomalous Diffusion of Harmful Information (CPP2021-008994) R&D grants. The work of Areg Mikael Sarvazyan has been partially developed with the support of valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, and co-founded by the European Union. The research at the Universitat Politecnica de Valencia was framed under the FairTransNLP research project, Grant PID2021-124361OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe.
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