A Systematic Survey on Instructional Text: From Representation Formats to Downstream NLP Tasks
This provides AI/NLP researchers with a comprehensive overview and unified view to bridge gaps in the emerging field of complex instruction processing.
The paper conducted a systematic survey of 177 papers to analyze complex instruction understanding in NLP, identifying trends, challenges, and opportunities in representation formats and downstream tasks.
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.