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SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

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arxiv 2404.14183 v1 pith:6TJRRKI2 submitted 2024-04-22 cs.CL

Yuxia Wang , Jonibek Mansurov , Petar Ivanov , Jinyan Su , Artem Shelmanov , Akim Tsvigun , Osama Mohammed Afzal , Tarek Mahmoud
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Giovanni Puccetti Thomas Arnold Chenxi Whitehouse Alham Fikri Aji Nizar Habash Iryna Gurevych Preslav Nakov
This is my paper · ORCID
classification cs.CL
keywords subtasktasktextmultilingualhumandetectiongeneratedmachine
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We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.

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Cited by 3 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. LuxVeri at GenAI Detection Task 3: Cross-Domain Detection of AI-Generated Text Using Inverse Perplexity-Weighted Ensemble of Fine-Tuned Transformer Models

    cs.CL 2025-01 conditional novelty 4.0 of 10

    A RoBERTa ensemble with inverse perplexity weighting achieved 0.826 TPR (non-adversarial) and 0.801 TPR (adversarial) in a cross-domain AI-text detection shared task.

  3. LuxVeri at GenAI Detection Task 1: Inverse Perplexity Weighted Ensemble for Robust Detection of AI-Generated Text across English and Multilingual Contexts

    cs.CL 2025-01 reject novelty 4.0 of 10

    An inverse perplexity weighted ensemble of fine-tuned transformers outperforms single models on English and multilingual AI-text detection, with Macro F1 0.7458 and 0.7513 respectively.

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