Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning to follow task instructions, i.e., instruction following. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize and provide insights to the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How to model instructions? (iii) What are popular instruction following datasets and evaluation metrics? (iv) What factors influence and explain the instructions' performance? (v) What challenges remain in instruction following? To our knowledge, this is the first comprehensive survey about instruction following.

翻译：任务语义可通过一组输入-输出示例或一段文本指令来表达。传统自然语言处理（NLP）的机器学习方法主要依赖大规模任务特定示例的可用性。这引发了两个问题：首先，当任务过于复杂或标注成本过高，或系统需要立即处理新任务时，收集任务特定的标注示例并不适用；其次，这种方式不够用户友好，因为终端用户可能更愿意在系统使用前提供任务描述，而非一组示例。因此，学界正日益关注一种新的NLP监督范式：学习遵循任务指令，即指令遵循。尽管该领域取得了令人瞩目的进展，但仍存在一些学界共同面临的普遍问题。本综述旨在总结当前指令遵循研究，并提供洞见，特别通过回答以下问题：（i）何为任务指令？存在哪些指令类型？（ii）如何对指令进行建模？（iii）有哪些常用的指令遵循数据集和评估指标？（iv）哪些因素影响并解释了指令的性能？（v）指令遵循仍面临哪些挑战？据我们所知，这是首篇关于指令遵循的全面综述。