Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often involve complex, multi-step instructions that remain challenging for current NLP systems. Robust understanding of such instructions is essential for deploying LLMs as general-purpose agents that can be programmed in natural language to perform complex, real-world tasks across domains like robotics, business automation, and interactive systems. Despite growing interest in this area, there is a lack of a comprehensive survey that systematically analyzes the landscape of complex instruction understanding and processing. Through a systematic review of the literature, we analyze available resources, representation schemes, and downstream tasks related to instructional text. Our study examines 181 papers, identifying trends, challenges, and opportunities in this emerging field. We provide AI/NLP researchers with essential background knowledge and a unified view of various approaches to complex instruction understanding, bridging gaps between different research directions and highlighting future research opportunities.
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Oxford（牛津大学）
SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models
School of Information Engineering, Yangzhou University（扬州大学信息工程学院）；Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service（江苏省知识管理与智能服务工程研究中心）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；SRIBD（深圳市大数据研究院）；University of Manchester（曼彻斯特大学）；University of Amsterdam（阿姆斯特丹大学）；Halmstad University（哈尔姆斯塔德大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Mila（米拉人工智能研究所）；University of Montreal（蒙特利尔大学）
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhongguancun Laboratory（中关村实验室）；National Financial Cryptography Research Center（国家金融密码研究中心）
International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Hong Kong University of Science and Technology（香港科技大学）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）
Tracing the Representation Geometry of Language Models from Pretraining to Post-training
Computer Science, McGill University（麦吉尔大学计算机科学系）；Mila - Quebec AI Institute（魁北克人工智能研究所）；UC Berkeley（加州大学伯克利分校）；G o o g l e , Paradigms of Intelligence Team（谷歌，智能范式团队）；Mathematics and Statistics, Université de Montréal（蒙特利尔大学数学与统计学系）；Neurology & Neurosurgery and Montreal Neurological Institute, McGill University（神经病学与神经外科及蒙特利尔神经研究所，麦吉尔大学）；CIFAR Learning in Machines & Brains Program（CIFAR 机器与大脑学习计划）
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
Southwest Jiaotong University（西南交通大学）；Microsoft Research Asia（微软亚洲研究院）；Institute of Computing Technology, Chinese Academy of Science（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）；Microsoft STCA（微软（中国）有限公司）
