Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges

• URL: http://arxiv.org/abs/2507.19672v1
• Date: Fri, 25 Jul 2025 20:52:58 GMT
• Title: Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
• Authors: Haoran Lu, Luyang Fang, Ruidong Zhang, Xinliang Li, Jiazhang Cai, Huimin Cheng, Lin Tang, Ziyu Liu, Zeliang Sun, Tao Wang, Yingchuan Zhang, Arif Hassan Zidan, Jinwen Xu, Jincheng Yu, Meizhi Yu, Hanqi Jiang, Xilin Gong, Weidi Luo, Bolun Sun, Yongkai Chen, Terry Ma, Shushan Wu, Yifan Zhou, Junhao Chen, Haotian Xiang, Jing Zhang, Afrar Jahin, Wei Ruan, Ke Deng, Yi Pan, Peilong Wang, Jiahui Li, Zhengliang Liu, Lu Zhang, Lin Zhao, Wei Liu, Dajiang Zhu, Xin Xing, Fei Dou, Wei Zhang, Chao Huang, Rongjie Liu, Mengrui Zhang, Yiwen Liu, Xiaoxiao Sun, Qin Lu, Zhen Xiang, Wenxuan Zhong, Tianming Liu, Ping Ma,
• Abstract summary: This survey provides a comprehensive overview of alignment techniques, training protocols, and empirical findings in large language models (LLMs) alignment.<n>We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives.<n>We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ)
• Score: 47.14342587731284
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment with human values and intentions has emerged as a critical challenge. This survey provides a comprehensive overview of practical alignment techniques, training protocols, and empirical findings in LLM alignment. We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives. Our analysis shows that while supervised fine-tuning enables basic instruction-following, preference-based methods offer more flexibility for aligning with nuanced human intent. We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ), highlighting their approaches to balancing quality and efficiency. We review existing evaluation frameworks and benchmarking datasets, emphasizing limitations such as reward misspecification, distributional robustness, and scalable oversight. We summarize strategies adopted by leading AI labs to illustrate the current state of practice. We conclude by outlining open problems in oversight, value pluralism, robustness, and continuous alignment. This survey aims to inform both researchers and practitioners navigating the evolving landscape of LLM alignment.

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