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
Blog post: Abliteration: Uncensoring LLMs via Weight Surgery
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:
- Direct blunt request
- Fictional/roleplay framing (novel, game, screenplay)
- Hypothetical/thought experiment
- Educational/research framing
- Professional cover story (pen tester, journalist, academic)
- Indirect ask (for a friend, to prevent something)
- Emotional framing (desperate, scared)
- Technical deep-dive (step-by-step, code, exact formulas)
- Subtle/coded language
- 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:
{"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.