arXiv AI By Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer, Salima Lamsiyah

AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection

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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.

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arXiv Computation and Language
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LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text

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is built from a diverse set of modern proprietary and open‑source LLMs to address gaps in existing resources, such as outdated models, limited language coverage, and lack of mixed human‑AI authorship data.

By Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Maksim Kuprashevich
arXiv AI
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Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

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.

By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu
Hugging Face Trending Papers
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Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

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.