YSGao/ReaMent

--- license: cc-by-nc-nd-4.0 task_categories: - text-classification language: - en tags: - agent pretty_name: ReaMent size_categories: - 1M<n<10M --- <h1>Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillation</h1> [](https://arxiv.org/abs/2505.15255)  ✨ Like ReaMent? Give us a ⭐ Star on GitHub! Your support keeps us going! [**Yuansheng-Gao/MentalMAD**](https://github.com/Yuansheng-Gao/MentalMAD) # 🌿 ReaMent Dataset Card A multi-round, real-world conversation-based mental manipulation detection dataset. # 🧠 Dataset Summary The ReaMent dataset was created to address the lack of real-world data in the field of mental manipulation detection. - **Source**: The dataset is built from the YTD-18M corpus, which contains over 18 million dialogue-like segments extracted from unscripted interactions in web videos. These dialogues cover a wide range of everyday scenarios, such as interviews, group discussions, and situational conversations. - **Size**: The final dataset consists of 5,000 high-quality annotated dialogues. - **Diversity**: ReaMent captures a broader range of conversational contexts compared to scripted data, providing more natural and spontaneous interaction patterns. - **Statistics**: Around 68.3% of dialogues in ReaMent were labeled as containing mental manipulation, while 31.7% were labeled as non-manipulative. The dataset has an average of 4 dialogue turns and 80 words per dialogue. # 🤗 Key Contributions - **Real-World Representation**: Unlike scripted or domain-specific datasets (e.g., MentalManip and LegalCon), ReaMent captures natural dialogues, making it valuable for detecting real-world mental manipulation. - **Scalability**: It complements smaller datasets, offering richer and more representative data for training models that aim to detect manipulative behaviors in social interactions. # 💻 Usage ```python from datasets import load_dataset ds = load_dataset("YSGao/ReaMent") ``` # 📝 Citation ```markdown @inproceedings{gao2026boosting, title={Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillation}, author={Gao, Yuansheng and Gao, Peng and Bao, Han and Li, Bin and Luo, Jixiang and Wang, Zonghui and Chen, Wenzhi}, booktitle={Proceedings of the ACM Web Conference 2026}, pages={9033--9043}, year={2026} } ```