Saved in:
| Main Authors: | McGlinchey, Andrea, Barclay, Peter J |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.21274 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction
by: McGlinchey, Andrea Cristina, et al.
Published: (2024)
by: McGlinchey, Andrea Cristina, et al.
Published: (2024)
Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing
by: Suvanto, Minerva, et al.
Published: (2026)
by: Suvanto, Minerva, et al.
Published: (2026)
A Rule-based Computational Model for Gaidhlig Morphology
by: Barclay, Peter J
Published: (2026)
by: Barclay, Peter J
Published: (2026)
Investigating Markers and Drivers of Gender Bias in Machine Translations
by: Barclay, Peter J, et al.
Published: (2024)
by: Barclay, Peter J, et al.
Published: (2024)
Real, Fake, or Manipulated? Detecting Machine-Influenced Text
by: Wang, Yitong, et al.
Published: (2025)
by: Wang, Yitong, et al.
Published: (2025)
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
by: Ma, Song-Duo, et al.
Published: (2026)
by: Ma, Song-Duo, et al.
Published: (2026)
Machine Learning Research Has Outpaced Its Communication Norms and NeurIPS Should Act
by: Rangarajan, Ajay Mandyam, et al.
Published: (2026)
by: Rangarajan, Ajay Mandyam, et al.
Published: (2026)
Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis
by: Xia, Yuxi, et al.
Published: (2026)
by: Xia, Yuxi, et al.
Published: (2026)
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation
by: Poey, Ian, et al.
Published: (2024)
by: Poey, Ian, et al.
Published: (2024)
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
by: Pedrotti, Andrea, et al.
Published: (2025)
by: Pedrotti, Andrea, et al.
Published: (2025)
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
by: Fang, Hao, et al.
Published: (2025)
by: Fang, Hao, et al.
Published: (2025)
Are AI-Generated Text Detectors Robust to Adversarial Perturbations?
by: Huang, Guanhua, et al.
Published: (2024)
by: Huang, Guanhua, et al.
Published: (2024)
FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models
by: Huang, Yue, et al.
Published: (2023)
by: Huang, Yue, et al.
Published: (2023)
Mixture of Detectors: A Compact View of Machine-Generated Text Detection
by: Lekkala, Sai Teja, et al.
Published: (2025)
by: Lekkala, Sai Teja, et al.
Published: (2025)
GPT-who: An Information Density-based Machine-Generated Text Detector
by: Venkatraman, Saranya, et al.
Published: (2023)
by: Venkatraman, Saranya, et al.
Published: (2023)
On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing
by: Liu, Yule, et al.
Published: (2024)
by: Liu, Yule, et al.
Published: (2024)
Detection of Fake Generated Scientific Abstracts
by: Theocharopoulos, Panagiotis C., et al.
Published: (2023)
by: Theocharopoulos, Panagiotis C., et al.
Published: (2023)
Can We Trust LLM Detectors?
by: Sandhan, Jivnesh, et al.
Published: (2026)
by: Sandhan, Jivnesh, et al.
Published: (2026)
Machine Text Detectors are Membership Inference Attacks
by: Koike, Ryuto, et al.
Published: (2025)
by: Koike, Ryuto, et al.
Published: (2025)
SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text Detoxification
by: Rykov, Elisei, et al.
Published: (2024)
by: Rykov, Elisei, et al.
Published: (2024)
LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning
by: Liu, Tao, et al.
Published: (2025)
by: Liu, Tao, et al.
Published: (2025)
Synthetic News Generation for Fake News Classification
by: Sittar, Abdul, et al.
Published: (2025)
by: Sittar, Abdul, et al.
Published: (2025)
Trace Is In Sentences: Unbiased Lightweight ChatGPT-Generated Text Detector
by: Mu, Mo, et al.
Published: (2025)
by: Mu, Mo, et al.
Published: (2025)
Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectors
by: Macko, Dominik, et al.
Published: (2025)
by: Macko, Dominik, et al.
Published: (2025)
MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors
by: Li, Yuanfan, et al.
Published: (2026)
by: Li, Yuanfan, et al.
Published: (2026)
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text
by: Schoenegger, Loris, et al.
Published: (2024)
by: Schoenegger, Loris, et al.
Published: (2024)
SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphs
by: Creo, Aldan, et al.
Published: (2024)
by: Creo, Aldan, et al.
Published: (2024)
MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text
by: Li, Chenjun, et al.
Published: (2026)
by: Li, Chenjun, et al.
Published: (2026)
Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation
by: Wang, Xinyu, et al.
Published: (2026)
by: Wang, Xinyu, et al.
Published: (2026)
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
by: Wang, Yichen, et al.
Published: (2024)
by: Wang, Yichen, et al.
Published: (2024)
Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts
by: Gritsai, German, et al.
Published: (2024)
by: Gritsai, German, et al.
Published: (2024)
AI Text Detectors and the Misclassification of Slightly Polished Arabic Text
by: Almohaimeed, Saleh, et al.
Published: (2025)
by: Almohaimeed, Saleh, et al.
Published: (2025)
A Practical Examination of AI-Generated Text Detectors for Large Language Models
by: Tufts, Brian, et al.
Published: (2024)
by: Tufts, Brian, et al.
Published: (2024)
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
by: Mireshghallah, Niloofar, et al.
Published: (2023)
by: Mireshghallah, Niloofar, et al.
Published: (2023)
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
by: Li, Guanghua, et al.
Published: (2024)
by: Li, Guanghua, et al.
Published: (2024)
MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models
by: Wang, Lionel Z., et al.
Published: (2024)
by: Wang, Lionel Z., et al.
Published: (2024)
RAFT: Realistic Attacks to Fool Text Detectors
by: Wang, James, et al.
Published: (2024)
by: Wang, James, et al.
Published: (2024)
Bengali Fake Reviews: A Benchmark Dataset and Detection System
by: Shahariar, G. M., et al.
Published: (2023)
by: Shahariar, G. M., et al.
Published: (2023)
LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning
by: Wang, Rongsheng, et al.
Published: (2024)
by: Wang, Rongsheng, et al.
Published: (2024)
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
by: Dugan, Liam, et al.
Published: (2024)
by: Dugan, Liam, et al.
Published: (2024)
Similar Items
-
Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction
by: McGlinchey, Andrea Cristina, et al.
Published: (2024) -
Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing
by: Suvanto, Minerva, et al.
Published: (2026) -
A Rule-based Computational Model for Gaidhlig Morphology
by: Barclay, Peter J
Published: (2026) -
Investigating Markers and Drivers of Gender Bias in Machine Translations
by: Barclay, Peter J, et al.
Published: (2024) -
Real, Fake, or Manipulated? Detecting Machine-Influenced Text
by: Wang, Yitong, et al.
Published: (2025)