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.

翻译：大语言模型的最新进展已展现出通过指令微调遵循简单指令的显著能力。然而，现实世界任务往往涉及当前自然语言处理系统尚难以处理的复杂多步骤指令。对于部署作为通用智能体的大语言模型而言，稳健理解此类指令至关重要——这些智能体可通过自然语言编程，在机器人技术、业务自动化及交互式系统等跨领域场景中执行复杂现实任务。尽管该领域日益受到关注，但目前仍缺乏系统梳理复杂指令理解与处理研究全貌的综合性综述。通过系统性文献回顾，我们分析了与指令文本相关的现有资源、表征方案及下游任务。本研究审查了181篇论文，识别出这一新兴领域的发展趋势、挑战与机遇。我们为人工智能/自然语言处理研究人员提供了必要的基础知识背景，并给出了面向复杂指令理解的不同方法的统一视角，从而弥合不同研究方向之间的鸿沟，凸显未来研究机遇。