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

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 However, progress is limited by a critical lack of suitable training data; existing datasets are often generated with outdated models, are predominantly in English, and fail to address the increasingly common scenario of mixed human-AI authorship.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 this https URL.

Computation and Language (cs.CL)

arXiv:2509.21269 [cs.CL]

(or arXiv:2509.21269v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.21269 Focus to learn more arXiv-issued DOI via DataCite

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