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Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains

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arxiv 2309.11285 v1 pith:DBZXNVEN submitted 2023-09-20 cs.CL cs.AIcs.LG

Areg Mikael Sarvazyan , José Ángel González , Marc Franco-Salvador , Francisco Rangel , Berta Chulvi , Paolo Rosso This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords autextificationtextoverviewdatasetdomainsiberleflanguagesmachine-generated
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark

    cs.CL 2026-01 conditional novelty 6.0 of 10

    Adding human-alignment augmentation (roleplaying, BPO, self-refine, RLDF) to machine-generated text both fools existing detectors and improves the generalization of detectors fine-tuned on it.

  2. When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Fine-tuned LLMs generate social media text that evades state-of-the-art detectors and human readers, dropping detection accuracy from up to 99.9% to near chance.

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