Create README.md

README.md

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+---
+license: other
+language:
+- en
+tags:
+- synthetic
+- jailbreak
+- refusal
+---
+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - en
+size_categories:
+  - 10M<n<100M
+pretty_name: LLM Refusal Training Dataset
+tags:
+  - safety
+  - refusal
+  - jailbreak-defense
+---
+
+# LLM Refusal Training Dataset
+
+A large-scale dataset designed to teach LLMs how to safely refuse jailbreak attempts, prompt injections, and policy-violating requests.
+
+## Dataset Description
+
+This dataset contains **30GB** of `(category, prompt, response)` triplets pairing simulated adversarial prompts with safe, helpful refusals. The data is non-operational and does not contain real exploits or harmful instructions.
+
+### Columns
+
+| Column     | Type   | Description                                                                 |
+|------------|--------|-----------------------------------------------------------------------------|
+| `category` | string | Attack taxonomy (e.g., `prompt_injection`, `roleplay_bypass`, `authority_claim`) |
+| `prompt`   | string | Simulated adversarial user message                                          |
+| `response` | string | Safe refusal with optional helpful redirection                              |
+
+### Categories Covered
+
+- Prompt injection & direct override
+- Roleplay / persona bypass
+- Authority & developer-mode claims
+- Encoding & obfuscation tricks
+- Hypothetical & fictional framing
+- Incremental escalation & multi-turn social engineering
+- Policy override & emotional pressure
+- Tool abuse & data exfiltration attempts
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "YOUR_USERNAME/llm-refusal-training-30gb",
+    data_files="data/llm_refusal_training.csv.gz",
+    split="train"
+)
+```
+
+### Intended Use
+1. Supervised fine-tuning for refusal behavior
+2. Safety alignment research
+3. Red-team evaluation benchmarks
+
+### Limitations
+
+1. May not capture all real-world attack distributions
+2. Refusals follow a limited set of patterns; consider diversifying for production use
+3. Not a substitute for human-curated safety data
+
+### License
+Open Data Attribution Training Disclosure License (ODATL‑1.0)