Machine-generated text: A comprehensive survey of threat models and detection methods

EN Crothers, N Japkowicz, HL Viktor - IEEE Access, 2023 - ieeexplore.ieee.org
Machine-generated text is increasingly difficult to distinguish from text authored by humans.
Powerful open-source models are freely available, and user-friendly tools that democratize …

Attribution and obfuscation of neural text authorship: A data mining perspective

A Uchendu, T Le, D Lee - ACM SIGKDD Explorations Newsletter, 2023 - dl.acm.org
Two interlocking research questions of growing interest and importance in privacy research
are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact …

A survey on LLM-generated text detection: Necessity, methods, and future directions

J Wu, S Yang, R Zhan, Y Yuan, LS Chao… - Computational …, 2025 - direct.mit.edu
The remarkable ability of large language models (LLMs) to comprehend, interpret, and
generate complex language has rapidly integrated LLM-generated text into various aspects …

The science of detecting LLM-generated text

R Tang, YN Chuang, X Hu - Communications of the ACM, 2024 - dl.acm.org
ACM: Digital Library: Communications of the ACM ACM Digital Library Communications of the
ACM Volume 67, Number 4 (2024), Pages 50-59 The Science of Detecting LLM-Generated Text …

Semeval-2024 task 8: Multidomain, multimodel and multilingual machine-generated text detection

Y Wang, J Mansurov, P Ivanov, J Su… - arxiv preprint arxiv …, 2024 - arxiv.org
We present the results and the main findings of SemEval-2024 Task 8: Multigenerator,
Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three …

MULTITuDE: Large-scale multilingual machine-generated text detection benchmark

D Macko, R Moro, A Uchendu, JS Lucas… - arxiv preprint arxiv …, 2023 - arxiv.org
There is a lack of research into capabilities of recent LLMs to generate convincing text in
languages other than English and into performance of detectors of machine-generated text …

M4gt-bench: Evaluation benchmark for black-box machine-generated text detection

Y Wang, J Mansurov, P Ivanov, J Su… - arxiv preprint arxiv …, 2024 - arxiv.org
The advent of Large Language Models (LLMs) has brought an unprecedented surge in
machine-generated text (MGT) across diverse channels. This raises legitimate concerns …

Targeted phishing campaigns using large scale language models

R Karanjai - arxiv preprint arxiv:2301.00665, 2022 - arxiv.org
In this research, we aim to explore the potential of natural language models (NLMs) such as
GPT-3 and GPT-2 to generate effective phishing emails. Phishing emails are fraudulent …

Multiscale positive-unlabeled detection of ai-generated texts

Y Tian, H Chen, X Wang, Z Bai, Q Zhang, R Li… - arxiv preprint arxiv …, 2023 - arxiv.org
Recent releases of Large Language Models (LLMs), eg ChatGPT, are astonishing at
generating human-like texts, but they may impact the authenticity of texts. Previous works …

Authorship obfuscation in multilingual machine-generated text detection

D Macko, R Moro, A Uchendu, I Srba, JS Lucas… - arxiv preprint arxiv …, 2024 - arxiv.org
High-quality text generation capability of recent Large Language Models (LLMs) causes
concerns about their misuse (eg, in massive generation/spread of disinformation). Machine …