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. Despite growing interest in this area, there lacks 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 177 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：大型语言模型的最新进展通过指令微调展示了遵循简单指令的潜力。然而，现实世界的任务通常涉及复杂、多步骤的指令，这对当前的自然语言处理系统仍具挑战性。尽管该领域日益受到关注，但仍缺乏对复杂指令理解与处理现状进行系统分析的全面综述。通过对文献的系统性梳理，我们分析了与教学文本相关的可用资源、表示方案及下游任务。本研究检视了177篇论文，识别了这一新兴领域的趋势、挑战与机遇。我们为人工智能/自然语言处理研究者提供了必要的背景知识，并对复杂指令理解的各种方法进行了统一梳理，弥合了不同研究方向之间的差距，并指明了未来的研究机遇。