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Task semantics can be expressed by a set of input-output examples or a pieceof textual instruction. Conventional machine learning approaches for naturallanguage processing (NLP) mainly rely on the availability of large-scale setsof task-specific examples. Two issues arise: first, collecting task-specificlabeled examples does not apply to scenarios where tasks may be too complicatedor costly to annotate, or the system is required to handle a new taskimmediately; second, this is not user-friendly since end-users are probablymore willing to provide task description rather than a set of examples beforeusing the system. Therefore, the community is paying increasing interest in anew supervision-seeking paradigm for NLP: learning to follow task instructions,i.e., instruction following. Despite its impressive progress, there are somecommon issues that the community struggles with. This survey paper tries tosummarize and provide insights to the current research on instructionfollowing, particularly, by answering the following questions: (i) What is taskinstruction, 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) Whatchallenges remain in instruction following? To our knowledge, this is the firstcomprehensive survey about instruction following.

Large Language Model Instruction Following: A Survey of Progresses and Challenges