---
license: apache-2.0
task_categories:
  - text-classification
tags:
  - abliteration
  - red-teaming
  - safety
  - synthetic
size_categories:
  - 1K<n<10K
language:
  - en
  - multilingual
---

# abliteration-harmful-enriched

Enriched harmful prompt dataset for abliteration (refusal direction identification). 7356 prompts across 33 categories, designed to provide broad coverage of the refusal subspace for more accurate direction estimation.

> **Used to produce:** [Bahushruth/Qwen3.6-35B-A3B-abliterated-v4](https://huggingface.co/Bahushruth/Qwen3.6-35B-A3B-abliterated-v4)
>
> **Blog post:** [Abliteration: Uncensoring LLMs via Weight Surgery](https://potatospudowski.github.io/articles/abliteration)

## Why This Dataset Exists

Standard abliteration datasets (e.g., `mlabonne/harmful_behaviors` with 520 prompts) are small and stylistically homogeneous. When you compute the "refusal direction" from these, you get a noisy estimate that captures surface-level prompt features rather than the true refusal mechanism. This leads to:
- Multi-direction approaches that overfit and destroy capabilities
- Single-direction approaches that miss the real refusal circuit

This dataset provides 14x more prompts with much greater diversity, making the mean-difference statistic converge to the genuine refusal direction. Result: **1 direction from this dataset achieves 0% refusal with zero capability damage** on Qwen3.6-35B-A3B.

## Dataset Composition

| Source | Count | Notes |
|--------|-------|-------|
| Synthetic (generated via LLM) | 6924 | 33 categories, ~210 per category |
| `mlabonne/harmful_behaviors` | ~430 | Original curated list |
| **Total** | **7356** | Train/test split |

## Prompt Diversity

Each category includes prompts across 10 styles:
1. Direct blunt request
2. Fictional/roleplay framing (novel, game, screenplay)
3. Hypothetical/thought experiment
4. Educational/research framing
5. Professional cover story (pen tester, journalist, academic)
6. Indirect ask (for a friend, to prevent something)
7. Emotional framing (desperate, scared)
8. Technical deep-dive (step-by-step, code, exact formulas)
9. Subtle/coded language
10. Non-English or mixed-language

Prompts also vary by length (1 sentence to 2-3 sentences) and persona (teens, professionals, researchers, writers, desperate people).

## Format

JSONL with fields:
```json
{"text": "the harmful prompt", "category": "malware and ransomware"}
```

## Intended Use

This dataset is designed **exclusively** for abliteration — computing the mean activation difference between harmful and harmless prompts to identify refusal directions in transformer residual streams. It is paired with a harmless dataset (e.g., `mlabonne/harmless_alpaca`) during the activation collection phase.

## Disclaimer

This dataset contains harmful prompts for AI safety research. It should not be used to train models to produce harmful content. It is released to enable reproducible abliteration research.