mlsanityData to 14 Oct 2026

Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text

arXiv 2403.05750 ↗First posted 9 Mar 2024cs.CL cs.AI cs.LGcited by 14

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Abstract (arXiv)

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 46 of its 64 references that are in the map. First posted on arXiv 9 Mar 2024.

Builds on

46 of 64 references.

References · 46

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34 more
  1. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  2. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  3. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  4. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 429
  5. 2019Explain Yourself! Leveraging Language Models for Commonsense Reasoningcited by 367
  6. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 360
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  8. 2023GPT detectors are biased against non-native English writerscited by 178
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  10. 2023On the Risk of Misinformation Pollution with Large Language Modelscited by 128
  11. 2020Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attackscited by 116
  12. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  13. 2021Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Modelscited by 105
  14. 2019Real or Fake? Learning to Discriminate Machine from Human Generated Textcited by 99
  15. 2023Testing of Detection Tools for AI-Generated Textcited by 90
  16. 2023On the Possibilities of AI-Generated Text Detectioncited by 88
  17. 2023Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Modelscited by 87
  18. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 82
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  20. 2023Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermarkcited by 67
  21. 2023RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thoughtcited by 47
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 10; 2026: 1.

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  1. 2025LLM Unlearning via Neural Activation Redirectioncited by 41
  2. 2025Rethinking Data Protection in the (Generative) Artificial Intelligence Eracited by 18
  3. 2025Feature-Level Insights into Artificial Text Detection with Sparse Autoencoderscited by 15
  4. 2025Detecting Stylistic Fingerprints of Large Language Modelscited by 11
  5. 2025Diversity Boosts AI-Generated Text Detectioncited by 6
  6. 2025FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprintingcited by 4
  7. 2024HULLMI: Human vs LLM identification with explainabilitycited by 3
  8. 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?cited by 3
  9. 2025AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Modelscited by 2
  10. 2025Self-reflecting Large Language Models: A Hegelian Dialectical Approachcited by 1
  11. 2025Position: It's Time to Act on the Risk of Efficient Personalized Text Generationcited by 1
  12. 2024ConvNLP: Image-based AI Text Detectioncited by 0
2 more
  1. 2024SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparisoncited by 0
  2. 2026Inverse Turing Bench: Evaluating Language Models as Judges of Human vs. AI Dialoguecited by 0