mlsanityData to 14 Oct 2026

DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts

arXiv 2405.10629 ↗First posted 17 May 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although there are a lot of existing detectors of AI content, they are often designed to give a binary answer and thus may not be suitable for more nuanced problem of finding the boundaries between human-written and machine-generated texts, while hybrid human-AI writing becomes more and more popular. In this paper, we address the boundary detection problem. Particularly, we present a pipeline for augmenting data for supervised fine-tuning of DeBERTaV3. We receive new best MAE score, according to the leaderboard of the competition, with this pipeline.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

23 of 35 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  6. 2020Longformer: The Long-Document Transformercited by 3,413
  7. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  8. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  9. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  10. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  11. 2019Defending Against Neural Fake Newscited by 559
  12. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
11 more
  1. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  2. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 261
  3. 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 166
  4. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 136
  5. 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 121
  6. 2021TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generationcited by 120
  7. 2023OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examplescited by 80
  8. 2022Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Textcited by 63
  9. 2023AI vs. Human -- Differentiation Analysis of Scientific Content Generationcited by 21
  10. 2023Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Educationcited by 17
  11. 2017Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attributioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?cited by 13
  2. 2024Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Textscited by 10
  3. 2026Beyond Detection: Rethinking Education in the Age of AI-writingcited by 0
  4. 2026Measuring Artifact Reliance in Machine-Generated Text Detectorscited by 0