MDD-TD: A dataset of large language model text source and content authenticity detection

Text detection is a significant research area in natural language processing, aiming to identify the source and authenticity of text content to help users access more credible information amidst the vast amount of online data. To address the scarcity of text detection datasets, this study constructs a Multi-Domain Text Detection Dataset (MDD-TD). The dataset comprises translation-optimized open-source data, web data collected through web crawling, and synthetic data generated using prompt-augmentation strategies. Translation data are obtained by translating and refining the question–answer corpus from the SimpleAI/HC3 dataset. Web-sourced data are collected from the Weibo and Douban platforms through web crawling. Synthetic data are generated following a rule-driven approach, leveraging existing translation and web-sourced data with multiple prompt enhancement strategies. To ensure data quality, this study employs the PPL method to remove texts with abnormal perplexity values. Semantic similarity–based deduplication is further applied further used, combined with manual review to conduct secondary filtering of low-quality samples. Ultimately, 31,721 high-quality samples are retained and stored in JSON format. Each sample contains three labels: “text”, “source”, and “label”, where “text” represents the text to be detected, “source” indicates the data origin, and “label” denotes the authenticity of the text. Evaluation experiments are conducted using two publicly available detection models, achieving an accuracy of 97.13% in the review domain for source detection and 98.87% in the news domain for authenticity detection. These results demonstrate that the dataset can serve as an effective resource for training and evaluating large language models in text detection tasks.