arXiv AI June 4, 2026 By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models

arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv Machine Learning

Jun 4

Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features

arXiv:2606. 04199v1 Announce Type: cross Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies.

By Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye, Dhrubajyoti Ghosh

llms

arXiv Computation and Language

Sep 15

SlopShape: Identifying AI-Generated Commercial Web Content

arXiv:2609.15369v1 Announce Type: new Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...

By Jochen Madler (Sitefire)

llms

arXiv AI

Jun 4

'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

arXiv:2606. 04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use.

By Nils Dycke, Marina Sakharova, Nico Daheim, Iryna Gurevych

benchmarks

arXiv Computation and Language

Sep 22

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...

By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann

llms fine-tuning

arXiv Machine Learning

2d ago

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...

By Luxu Liang, Xiang Li

llms safety

arXiv AI

Sep 1

Interpretable Predictability-Based AI Text Detection: A Replication Study

arXiv:2603.15034v2 Announce Type: replace-cross Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. E...

By Adam Skurla, Dominik Macko, Jakub Simko

llms benchmarks