梳理复杂指令理解研究现状，助力AI实现跨领域自然语言编程
Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents
- 系统分析181篇论文，归纳指令表示与任务类型
- 揭示当前模型在多步指令理解上的显著不足
- 适合关注AI代理与自然语言编程的研究者
大型语言模型在简单指令遵循方面已展现良好能力，但真实场景中的任务往往涉及复杂、多步骤的指令，仍是当前NLP系统的挑战。对这类指令的稳健理解是将大模型部署为通用智能体的关键，使它们能通过自然语言被编程以完成跨机器人、业务自动化及交互系统等领域的复杂任务。尽管该领域兴趣日益增长，但缺乏系统性综述来全面分析复杂指令理解与处理的研究现状。通过对181篇文献的系统性回顾，本文分析了可用资源、表示方法及下游任务，揭示了该新兴领域的趋势、挑战与机遇。研究为人工智能与自然语言处理研究者提供了必要的背景知识和统一视角，弥合不同研究方向间的差距，并指明未来研究方向。
原文摘要 · Abstract (English)
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.
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