arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.
The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents. Existing methods for detecting LLM-generated text mainly focus on document-level classification and cannot identify which parts of the text are generated by LLMs.
LLMTrace is a new large‑scale bilingual (English and Russian) corpus designed to improve AI‑written text detection. It contains character‑level annotations that enable precise localization of AI‑generated segments, supporting both full‑text binary classification and interval detection tasks. The dataset is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.
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.
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.
The paper presents a large‑scale empirical study of 284 interpretable linguistic features to detect AI‑generated English text across 30 language models and 10 text domains. It finds that classifiers using only linguistic features can reliably distinguish machine‑from human‑written text, but most indicators are highly context‑dependent. Only lexical richness measures consistently generalize across model families and domains, offering robust signals for detection.
The paper introduces SHAKTI, a large-scale Hindi benchmark for detecting AI-generated text, covering multiple genres, LLM families, prompting strategies, and adversarial transformations. It evaluates the benchmark with human annotation and examines linguistic, grammatical, and sentiment differences between human and AI texts. Building on these insights, the authors propose TRIDENT, a round‑trip translation‑based detection framework with three variants that outperforms existing baselines and demonstrates robustness across seven languages and adversarial settings.
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...
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...
The paper introduces a discrete-class framework for detecting human‑AI co‑authorship, distinguishing between superficial AI polishing and deeper AI‑driven discourse changes. It shows that adversarial paraphrasing can obscure detection signals, but augmenting training data with such paraphrases improves detector robustness across multiple datasets and generation models. The study demonstrates that principled data augmentation and a fine‑grained classification approach are essential for reliable co‑authorship detection.
arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.
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...