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@@ -5,3 +5,70 @@ configs:
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  - config_name: safe
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  data_files: harmful/beavertails_unsafe_cot.json
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - config_name: safe
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  data_files: harmful/beavertails_unsafe_cot.json
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  ---
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+
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+ # BeaverTails Safety Fine-tuning Dataset
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+
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+ ## Dataset Overview
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+ This dataset extends the BeaverTails harmful QA evaluation benchmark by providing **paired safe responses** for harmful prompts alongside safe prompts.
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+
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+ - **Harmful subset** (`config_name: "harmful"`): Unique harmful prompts extracted from BeaverTails + generated safe refusal responses categorized into 14 safety violation types.
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+ - **Safe subset** (`config_name: "safe"`): Safe prompts + ambiguous prompts that appear harmful but are safe, all paired with appropriate safe responses.
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+
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+ ## Safety Categories (Harmful Refusals)
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+ Safe refusals for harmful prompts are organized by these 14 categories:
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+ - animal_abuse
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+ - child_abuse
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+ - controversial_topics,politics
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+ - discrimination,stereotype,injustice
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+ - drug_abuse,weapons,banned_substance
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+ - financial_crime,property_crime,theft
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+ - hate_speech,offensive_language
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+ - misinformation_regarding_ethics,laws_and_safety
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+ - non_violent_unethical_behavior
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+ - privacy_violation
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+ - self_harm
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+ - sexually_explicit,adult_content
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+ - terrorism,organized_crime
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+ - violence,aiding_and_abetting,incitement
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+
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+
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+ ## Usage
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+ Load specific subsets:
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Harmful prompts + safe refusals
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+ ds_harmful = load_dataset("yourusername/yourrepo", "harmful")
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+
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+ # Safe prompts + safe responses
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+ ds_safe = load_dataset("yourusername/yourrepo", "safe")
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+
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+ # Full dataset (both configs)
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+ ds = load_dataset("yourusername/yourrepo")
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+ ```
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+
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+ ## Intended Use
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+ **Primary**: Supervised fine-tuning (SFT) of small language models (SLMs) to improve safe text generation.
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+ **Benefits**:
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+ - Teaches models to recognize harmful intent and respond with safe refusals
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+ - Provides safe alternatives for ambiguous prompts
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+ - Critical for SLM deployment in user-facing applications
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+ - Addresses rising need for model safety in production environments
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+
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+ ## Dataset Structure
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+ Each example contains:
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+ - `prompt`: Harmful/safe prompt text
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+ - `response`: Corresponding safe response (refusal for harmful, completion for safe)
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+ - `category`: Safety violation type (harmful subset only)
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+
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+ ## Responsible Use
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+ ⚠️ **Harmful prompts are included for safety training only.** Do not use raw harmful prompts for generation. Always validate model outputs before production deployment.
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+
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+ ## Creation Process
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+ - Extracted all unique harmful prompts from the original BeaverTails dataset
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+ - Generated safe refusal responses for harmful prompts (14 categories)
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+ - Curated safe prompts including ambiguous-but-safe examples
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+ - Formatted as instruction-response pairs for SFT
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+