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arXiv · 2509.21269

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

Irina Tolstykh·Aleksandra Tsybina·Sergey Yakubson·Maksim Kuprashevich
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Abstract

The widespread use of human-like text from Large Language Models (LLMs) necessitates the development of robust detection systems. However, progress is limited by a critical lack of suitable training data; existing datasets are often generated with outdated models, are predominantly in English, and fail to address the increasingly common scenario of mixed human-AI authorship. Crucially, while some datasets address mixed authorship, none provide the character-level annotations required for the precise localization of AI-generated segments within a text. To address these gaps, we introduce LLMTrace, a new large-scale, bilingual (English and Russian) corpus for AI-generated text detection. Constructed using a diverse range of modern proprietary and open-source LLMs, our dataset is designed to support two key tasks: traditional full-text binary classification (human vs. AI) and AI-generated interval detection, facilitated by character-level annotations. We believe LLMTrace will serve as a vital resource for training and evaluating the next generation of more nuanced and practical AI detection models. The project page is available at https://sweetdream779.github.io/LLMTrace-info/.

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Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Maksim Kuprashevich. 2026-08-26. LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text. https://arxiv.org/abs/2509.21269

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