mlsanityData to 14 Oct 2026

Artificial Text Detection with Multiple Training Strategies

arXiv 2212.05194 ↗First posted 10 Dec 2022cs.CL cs.AIcited by 4

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Abstract (arXiv)

As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate product reviews, fake news, and even fake political content. The paper proposes a solution for the Russian Artificial Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish which model within the list is used to generate this text. We introduce the DeBERTa pre-trained language model with multiple training strategies for this shared task. Extensive experiments conducted on the RuATD dataset validate the effectiveness of our proposed method. Moreover, our submission ranked second place in the evaluation phase for RuATD 2022 (Multi-Class).

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 10 Dec 2022.

Builds on

17 of 27 references.

References · 17

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5 more
  1. 2021Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuningcited by 146
  2. 2021Artificial Text Detection via Examining the Topology of Attention Mapscited by 49
  3. 2020A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Modelscited by 30
  4. 2022Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russiancited by 29
  5. 2021Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNetcited by 27

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 3.

4 papers.

  1. 2025DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressivelycited by 63
  2. 2023Generative AI Text Classification using Ensemble LLM Approachescited by 29
  3. 2025AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classificationcited by 3
  4. 2025T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Textcited by 1