arXiv AI
Interpretable Predictability-Based AI Text Detection: A Replication Study
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arXiv AI
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Authorship Attribution in Multilingual Machine-Generated Texts
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.
arXiv AI
Sep 10
AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths
arXiv:2609.06771v1 Announce Type: cross Abstract: Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation i...
arXiv AI
Jun 2
AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
arXiv:2606. 00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals.
arXiv Computation and Language
Aug 31
AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not
The study demonstrates that text produced by large language models (LLMs) leaves a distinct stylometric footprint—primarily increased entropy and lexical diversity—across multiple models and domains. In contrast, AI editing of human text does not replicate this footprint; edited texts show only modest lexical diversity gains and reduced entropy, with lexical density emerging as the key distinguishing feature. Consequently, stylometric analysis can differentiate AI-generated from AI-edited content, but is less effective at distinguishing either from purely human writing.
arXiv AI
Jun 4
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.
arXiv Machine Learning
Jun 5
Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.