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A Survey of Large Language Models
Wayne Xin Zhao (Renmin University of China), Kun Zhou (Renmin University of China), Junyi Li (Renmin University of China), Tianyi Tang (Renmin University of China), Zican Dong (Renmin University of China), Yupeng Hou (Renmin University of China), Beichen Zhang (Renmin University of China), Yingqian Min (Renmin University of China), Junjie Zhang (Renmin University of China), Peiyu Liu (Renmin University of China), Xiaolei Wang (Renmin University of China), Yifan Du (Renmin University of China), Yushuo Chen (Renmin University of China), Yushuo Chen (Renmin University of China), Zhipeng Chen (Renmin University of China), Jinhao Jiang (Renmin University of China), Ruiyang Ren (Renmin University of China), Yifan Li (Renmin University of China), Xinyu Tang (Renmin University of China), Peiyu Liu (Renmin University of China), Yiwen Hu (Renmin University of China), Jian‐Yun Nie (Université de Montréal), Ji-Rong Wen (Renmin University of China)
Résumé : Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervised fine-tuning and reinforcement learning, which adapt foundational models to downstream tasks and enhance their alignment and safety; (3) utilization strategies, such as in-context learning, prompt engineering, and agentic reasoning, that optimize real-world deployment and enable effective interaction with external environments; and (4) evaluation methods, encompassing benchmarks for key ability dimensions such as core language capabilities, reasoning, and safety, which support comprehensive and reliable assessment of model performance. Additionally, we identify critical research issues, including those concerning theoretical foundations, efficient scaling, alignment, and agentic capability, and highlight the open challenges they present. By synthesizing state-of-the-art insights and emerging trends, this survey aims to provide a systematic and comprehensive framework for understanding the trajectory, current limitations, and future directions of LLM progress.