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: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:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.
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is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.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: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.
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...
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.
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: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:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?
MultiGhostBench is a multilingual benchmark for authorship attribution of long-form text generated by large language models. It contains 928 books produced by five recent LLMs in six languages and three scripts, each averaging about 59,000 words, and is designed to test attribution methods under domain, author, and language shifts. Experiments show that no single attribution method dominates across all settings, with performance generally dropping under distribution shifts, and that transformer-based detectors retain generator information across languages while statistical and fingerprint-based detectors are more language‑dependent.
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...
MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.