Alignment of Large Language Models (LLMs) remains an unsolved problem. Human preferences are highly distributed and can be captured at multiple levels of abstraction, from the individual to diverse populations. Organisational preferences, represented by standards and principles, are defined to mitigate reputational risk or meet legislative obligations. In this paper, we present ABC Align, a novel alignment methodology for LLMs that enables integration of the standards and preferences of a large media organisation into the LLM itself. We combine a set of data and methods that build on recent breakthroughs in synthetic data generation, preference optimisation, and post-training model quantisation. Our unified approach mitigates bias and improves accuracy, while preserving reasoning capability, as measured against standard benchmarks.
Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons
Department of Computer Science and Technology, BNRist（计算机科学与技术系，BNRist）；Shenzhen International Graduate School（深圳国际研究生院）；KIRC, Institute for Artificial Intelligence, Tsinghua University（人工智能研究院，清华大学）
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；National University of Singapore（新加坡国立大学）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review
African Institute for Mathematical Sciences (AIMS)（非洲数学科学研究所）；Bayero University Kano（卡诺贝罗大学）；Brown University（布朗大学）；Centrum Wiskunde & Informatica（数学与计算机科学中心）；University of Pretoria（比勒陀利亚大学）；University of Hamburg（汉堡大学）；Imperial College London（伦敦帝国学院）
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
School of Electronics and Computer Science, University of Southampton, UK（索姆塞特大学电子与计算机科学学院）；Department of Computer Science, University of Liverpool, UK（利弗pool大学计算机科学系）
Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model
Safety Alignment via Constrained Knowledge Unlearning
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；University of Macau, China（澳门大学）；Nankai University, China（南开大学）；Zhejiang University, China（浙江大学）；Peng Cheng Laboratory, China（鹏城实验室）；Shaqra University, Saudi Arabia（沙克拉大学）
Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron
Beijing Institute of AI Safety and Governance（北京人工智能安全与治理研究院）；Beijing Key Laboratory of Safe AI and Superalignment（北京安全人工智能与超对齐重点实验室）；Ant Group Co., Ltd.（蚂蚁集团有限公司）；BrainCog Lab., CASIA（CASIA脑认知实验室）；Zhongguancun Academy（中关村学院）
SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment
Beihang University（北航）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Rethinking the Evaluation of Alignment Methods: Insights into Diversity, Generalisation, and Safety
Wroclaw University of Science and Technology (WUST)（沃拉布大学科学与技术学院）；National Research Institute (NASK)（国家研究 institute）；Institute of Computer Science, Polish Academy of Sciences (IPI PAN)（波兰科学院计算机科学研究所）
Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Johns Hopkins University（约翰霍普金斯大学）；Michigan State University（密歇根州立大学）；University of Mississippi（密苏里州立大学）
Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment
Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；University of New South Wales（新南威尔士大学）；Nankai University（南开大学）
Safety Alignment Should Be Made More Than Just A Few Attention Heads
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
MPO: Multilingual Safety Alignment via Reward Gap Optimization
Harbin Institute of Technology（哈尔滨工业大学）；Singapore Management University（新加坡管理学院）；Monash University（墨尔本大学）；Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）
Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization
The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高科技园区（滨江）区块链与数据安全研究院）；Sun Yat-sen University（中山大学）；KAUST（卡塔尔大学）
