README.md

---
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> 

[![Paper](https://img.shields.io/badge/arXiv-2512.01282-b31b1b.svg)](https://arxiv.org/abs/2505.15255)
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# 🌿 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
@misc{gao2026boostinglargelanguagemodels,
      title={Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillation}, 
      author={Yuansheng Gao and Peng Gao and Han Bao and Bin Li and Jixiang Luo and Zonghui Wang and Wenzhi Chen},
      year={2026},
      eprint={2505.15255},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.15255}, 
}
```