Large Language Models (LLM) alignment aims to prevent models from producing content that misaligns with human expectations, which can lead to ethical and legal concerns. In the last few years, Reinforcement Learning from Human Feedback (RLHF) has been the most prominent method for achieving alignment. Due to challenges in stability and scalability with RLHF stages, which arise from the complex interactions between multiple models, researchers are exploring alternative methods to achieve effects comparable to those of RLHF. However, these methods often rely on large high-quality datasets. Despite some methods considering the generation of additional data to expand datasets, they often treat model training and data generation as separate and static processes, overlooking the fact that these processes are highly interdependent, leading to inefficient utilization of the generated data. To deal with this problem, we propose PLE, i.e., Progressively Label Enhancement for LLM Alignment, a framework that dynamically adjusts the model's training process based on the evolving quality of the generated data. Specifically, we prompt the model to generate responses for both the original query and the query guided by a set of carefully designed principles, and then utilize a dynamic threshold to determine the appropriate training approach for both responses based on their corresponding reward scores. Experimental results demonstrate the effectiveness of PLE compared to existing LLM alignment methods.
AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；State University of New York at Buffalo（纽约州立大学布法罗分校）；Huawei International, Singapore（新加坡华为国际）
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
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）；BAAI（北京智源人工智能研究院）；University of Waterloo（滑铁卢大学）；M-A-P；The University of Manchester（曼彻斯特大学）；Peking University（北京大学）
SafeEvo: Deciphering the Safety Alignment Mechanism and Evolution in Language Models
The University of Hong Kong (HKU)（香港大学）；Chinese Academy of Sciences (CAS)（中国科学院）；Information Engineering University (IEU)（信息工程大学）；Nanyang Technological University (NTU)（南洋理工大学）
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, China（通用人工智能国家重点实验室，智能科学与技术学院，北京大学，中国）；Huawei Noah’s Ark Lab, China（华为诺亚方舟实验室，中国）
Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China（湖北人工智能与智能学习省级重点实验室，中央财经大学，武汉，中国）；School of Computer Science, Central China Normal University, Wuhan, China（中央财经大学计算机科学学院，武汉，中国）；National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, China（网络媒体语言资源监测与研究中心，中央财经大学，武汉，中国）；College of Computer Science, Inner Mongolia University, Hohhot, China（内蒙古大学计算机学院，呼和浩特，中国）；National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, Inner Mongolia University, Hohhot, China（蒙古语智能信息处理技术国家与地方联合工程研究中心，内蒙古大学，呼和浩特，中国）；Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology, Inner Mongolia University, Hohhot, China（内蒙古多语言人工智能技术重点实验室，内蒙古大学，呼和浩特，中国）
CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training
Department of Computer Science and Engineering, Korea University（韩国大学计算机科学与工程系）；Department of English Language and Literature, Kyungpook National University（庆北国立大学英语语言文学系）
Bridging the Stability-Expressivity Gap: Synthetic Data Scaling and Preference Alignment for Low-Resource Spoken Language Models
Beijing University of Posts（北京邮电大学）；University of California, USA（美国加州大学）；Northwestern University, USA（美国西北大学）；Eastern Institute of Technology, Ningbo, China（宁波工程技术学院）
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue
PAII Inc.（PAII公司）；Ping An Technology（平安科技）；Ping An Healthcare and Technology Company Limited（平安医疗健康科技有限公司）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tianjin University（天津大学）
