1. Introduction  
1.1. Strategy for Literature Search  
1.2. Comparisons with Other Surveys  
1.3. The Main Contributions of the Survey  
1.4. The Outline of the Survey  
2. Background  
2.1. Model Architecture  
2.2. In-Context Learning  
3. Data Safety  
3.1. Toxicity  
3.2. Bias  
3.3. Data Privacy  
3.4. Copyright  
4. Model Safety  
4.1. Misinformation  
4.2. Evaluation Problem  
4.3. Explainability  
4.4. Inference Privacy  
5. Prompt Safety  
5.1. Prompt Injection and Jailbreaking  
5.2. Guardrails  
6. AI Alignment  
6.1. Philosophy of Alignment: Ethics and Morality  
6.2. Agentic Alignment  
6.3. Defining Alignment for the Linguistic Aspect of Large Language Models  
6.4. Generating Content that Aligns with Human  
6.5. Methods to Align LLMs  
7. Safety at Scale  
7.1. Scalable Oversight  
7.2. Emergent Abilities  
7.3. Knowledge Distillation  
7.4. Catastrophic Forgetting  
8. Future Work  
9. Conclusion