mlsanityData to 7 Oct 2026

TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques

arXiv 2403.16592 ↗First posted 25 Mar 2024cs.CLcited by 3

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Abstract (arXiv)

The Large Language Models (LLMs) exhibit remarkable ability to generate fluent content across a wide spectrum of user queries. However, this capability has raised concerns regarding misinformation and personal information leakage. In this paper, we present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains in both mono-lingual and multi-lingual contexts. Our study comprehensively analyzes various methods to detect machine-generated text, including statistical, neural, and pre-trained model approaches. We also detail our experimental setup and perform a in-depth error analysis to evaluate the effectiveness of these methods. Our methods obtain an accuracy of 86.9% on the test set of subtask-A mono and 83.7% for subtask-B. Furthermore, we also highlight the challenges and essential factors for consideration in future studies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

32 of 45 references.

References · 32

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20 more
  1. 2019Release Strategies and the Social Impacts of Language Modelscited by 505
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detectioncited by 30
  2. 2025M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Textcited by 2
  3. 2026AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detectioncited by 0