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

• URL: http://arxiv.org/abs/2404.14183v1
• Date: Mon, 22 Apr 2024 13:56:07 GMT
• Title: SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
• Authors: Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Chenxi Whitehouse, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov,
• Abstract summary: Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. 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.
• Score: 68.858931667807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GigaCheck: Detecting LLM-generated Content [72.27323884094953]
  In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize AI-generated intervals within text.
  arXiv  Detail & Related papers  (2024-10-31T08:30:55Z)
• PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text? [4.463184061618504]
  We present our submission to the SemEval-2024 Task 8 "Multigenerator, Multidomain, and Black-Box Machine-Generated Text Detection" Our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. Our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91.
  arXiv  Detail & Related papers  (2024-04-08T13:05:02Z)
• SemEval-2024 Task 8: Weighted Layer Averaging RoBERTa for Black-Box Machine-Generated Text Detection [5.049812996253857]
  This document includes the details of the authors' submission to the proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-generated Text Detection Subtask A (monolingual) and B.
  arXiv  Detail & Related papers  (2024-02-24T17:44:56Z)
• Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection [3.6433784431752434]
  SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C)
  arXiv  Detail & Related papers  (2024-01-22T19:39:05Z)
• Multitask Multimodal Prompted Training for Interactive Embodied Task Completion [48.69347134411864]
  Embodied MultiModal Agent (EMMA) is a unified encoder-decoder model that reasons over images and trajectories. By unifying all tasks as text generation, EMMA learns a language of actions which facilitates transfer across tasks.
  arXiv  Detail & Related papers  (2023-11-07T15:27:52Z)
• Findings of the WMT 2022 Shared Task on Translation Suggestion [63.457874930232926]
  We report the result of the first edition of the WMT shared task on Translation Suggestion. The task aims to provide alternatives for specific words or phrases given the entire documents generated by machine translation (MT) It consists two sub-tasks, namely, the naive translation suggestion and translation suggestion with hints.
  arXiv  Detail & Related papers  (2022-11-30T03:48:36Z)
• Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian [6.9244605050142995]
  We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The dataset includes texts from 14 text generators, i.e., one human writer and 13 text generative models fine-tuned for one or more of the following generation tasks. The human-written texts are collected from publicly available resources across multiple domains.
  arXiv  Detail & Related papers  (2022-06-03T14:12:33Z)
• FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue [70.65782786401257]
  This work explores conversational task transfer by introducing FETA: a benchmark for few-sample task transfer in open-domain dialogue. FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs.
  arXiv  Detail & Related papers  (2022-05-12T17:59:00Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection [27.45642971636561]
  OffensEval 2020 task includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C) Our submissions achieve F1 scores of 91.7% in Sub-task A, 66.5% in Sub-task B, and 63.2% in Sub-task C.
  arXiv  Detail & Related papers  (2020-07-28T20:45:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.