Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment

arXiv:2609.30802 · cs.CL · Submitted 2026-09-25 · Read on arXiv

cs.CL

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/anjilab/role-of-prompt-template-in-kd

Terminology

• Distillation — 67× in this paper · explained in 2 episodes
• Alignment — 35× in this paper · explained in 1 episode
• Knowledge Distillation — 22× in this paper · explained in 11 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Attack Success Rate — 5× in this paper
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Edge Devices — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• A General Language Assistant as a Laboratory for Alignment
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• The Llama 3 Herd of Models
• Distilling the Knowledge in a Neural Network
• Mistral 7B
• Qwen2.5 Technical Report
• Gemma 2: Improving Open Language Models at a Practical Size
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Universal and Transferable Adversarial Attacks on Aligned Language Models

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