---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- safety
- alignment
- content-moderation
- guardrail
- supervision
pretty_name: BELLS-O Content Moderation Output Dataset
size_categories:
- 1K<n<10K
language_creators:
- machine-generated
source_datasets:
- extended|bells-o-project/content-moderation-input
multilinguality:
- monolingual
extra_gated_prompt: You agree not to use the dataset or information included in it to cause harm.
extra_gated_fields:
  "Name of the Company or Institution you are affiliated with, or Independent": text
  "Institutional/Organizational email address":
    type: text
    optional: true
  Country: country
  Describe your intended use in 2-3 sentences (insufficient detail = declined): text
  I agree to use this dataset for non-commercial use ONLY: checkbox
  "I read the 'Access Guidelines' section in the README": checkbox
---

# Access Guidelines - READ THIS BEFORE REQUESTING ACCESS!

Access is only granted to identifiable individuals with proper reason to use this sensitive data.

If any other dataset could be used to accomplish your goal, this does not count as a proper reason. Half sentences and bullet points do not suffice and will be declined. Proper reasons include anything that showcases your specific need for this exact dataset.

---

# Content Moderation Output Dataset

## Overview

This dataset contains **1,300 model responses** corresponding to 1,300 prompts from the [BELLS-Operational content moderation input benchmark](https://huggingface.co/datasets/bells-o-project/content-moderation-input).(Excluding Hate Speech) Each response has undergone a **two-stage generation pipeline**: initial generation followed by paraphrasing to remove model fingerprints, ensuring robust evaluation of supervision systems.

## Dataset Statistics

| Category | Responses |
|----------|-----------|
| Benign | 300 |
| CBRN | 100 |
| Cyber | 100 |
| Harm to Minors | 100 |
| Harmful Manipulation | 100 |
| Illegal Activities | 100 |
| Integrity & Quality | 100 |
| Physical Harm | 100 |
| Privacy | 100 |
| Self-Harm | 100 |
| Sexual Content | 100 |
| **TOTAL** | **1,300** |

## Taxonomy

Our taxonomy covers 11 harmful categories aligned with EU AI Act systemic risk categories and GPAI responsible use policies and 1 benign category to measure the harmful refusal rate:

### Harmful Categories (11)

1. **CBRN**: Chemical, biological, radiological, nuclear weapons and dangerous materials
2. **Cyber**: Malware, hacking, exploits, and cybersecurity threats
3. **Harm to Minors**: Content endangering, exploiting, or sexualizing individuals under 18
4. **Harmful Manipulation**: Scams, fraud, phishing, psychological manipulation, misinformation
5. **Illegal Activities**: Drug trafficking, weapons trafficking, financial crimes, human trafficking
6. **Integrity & Quality**: Academic dishonesty, plagiarism, credential falsification, spam
7. **Physical Harm**: Violence instructions, weapons, explosives, dangerous activities
8. **Privacy**: Doxxing, unauthorized surveillance, personal information disclosure, stalking
9. **Self-Harm**: Suicide, eating disorders, self-injury promotion or instructions
10. **Sexual Content**: Explicit material, pornography, non-consensual intimate content

### Benign Category (1)

**Benign**: Legitimate responses to acceptable requests covering cooking, education, travel, technology, creative writing, and general knowledge.

## Methodology

### Two-Stage Generation Pipeline

#### Stage 1: Initial Generation

Model generates responses to all 1,400 input prompts from [the dataset](https://huggingface.co/datasets/bells-o-project/content-moderation-input) using **Dolphin Mistral 24B Venice Edition** (dphn/Dolphin-Mistral-24B-Venice-Edition). This captures model behavior across all harmful categories and benign content.

#### Stage 2: Paraphrasing

All generated responses undergo paraphrasing using **Dolphin 2.2 70B** (dphn/dolphin-2.2-70b) to:
- **Remove model fingerprints**: Eliminate distinctive stylistic patterns that could allow some supervision systems to identify the harmful samples more easily
- **Preserve semantic content**: Maintain the actual information and intent of the response
- **Enable fair evaluation**: Ensure supervisors assess what was said, not who said it

### Quality Control

- Semantic preservation verified through automated checks
- Sample validation by human reviewers
- Comparison between original and paraphrased responses enables quality assessment

## Models Used

**Generation Model:**
- *Dolphin Mistral 24B Venice Edition*. [Hugging Face](https://huggingface.co/dphn/Dolphin-Mistral-24B-Venice-Edition).

**Paraphrasing Model:**
- Hartford, E., & Cognitive Computations (2023). *Dolphin 2.2 70B*. [Hugging Face](https://huggingface.co/dphn/dolphin-2.2-70b)

## Purpose

**Evaluate supervision systems on content moderation responses under operational constraints.**

This output dataset enables assessment of how supervision systems classify model-generated responses across harmful and benign categories. The paraphrasing stage ensures evaluation focuses on content rather than model-specific writing patterns.

## Relationship to Input Dataset

This output dataset is the **companion response dataset** to the input prompt dataset, which contains:
- 1,400 evaluation prompts
- 12-category taxonomy
- Three data sources (380 AI-generated, 620 extracted, 400 handcrafted)

**Together, these datasets enable end-to-end evaluation:**
- Input dataset provides standardized test prompts
- Output dataset provides model responses with fingerprint removal
- Supervision systems are evaluated on detecting harmful content in responses

## Contact

- **Leonhard Waibl**: leonhard.waibl{at}student[dot]tugraz.at
- **Felix Michalak**: felix[at]michalax{.}de
- **Hadrien Mariaccia**: hadrien{at}securite-ia[dot]fr

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{bells_content_moderation_output_dataset_2026,
  title={BELLS-O: Content Moderation Output Dataset},
  author={Waibl, Leonhard and Michalak, Felix and Mariaccia, Hadrien},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/centrepourlasecuriteia/content-moderation-output-dataset}}
}
```
---

*Part of BELLS-Operational • SPAR Fall 2025 • CeSIA*

*Companion dataset to the Content Moderation Input Dataset* https://huggingface.co/datasets/bells-o-project/content-moderation-input