发表机构
Shanghai Jiao Tong University; Nanjing University; Shanghai Artificial Intelligence Laboratory（上海交通大学; 南京大学; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
综述从语义复杂性角度审视人工智能语义智能发展，系统调研高级语义任务研究，总结理解和生成的相关方法及策略，旨在推动人工智能向高级语义智能持续发展。
AI 中文摘要
人工智能的最新进展极大地扩展了其认知和推理能力。从语义复杂性角度看，人工智能发展呈现从简单到复杂语义处理的轨迹，即从基础语义智能向高级语义智能转变。此前工作未系统全面研究该问题。本综述从语义复杂性审视人工智能语义智能发展，系统调研高级语义任务研究，总结理解和生成的相关方法及策略，旨在推动人工智能向高级语义智能持续发展。
英文摘要
Recent advances in AI have substantially expanded its cognitive and reasoning capabilities. From the perspective of semantic complexity, the development of AI reveals a clear trajectory from simple to complex semantic processing. While early AI systems mainly addressed tasks involving direct and literal semantic perception or expression, contemporary systems are increasingly expected to perform more sophisticated cognitive reasoning, enabling the understanding and generation of High-Level Semantics (HLS). A similar trajectory can also be observed in human cognitive development. We define this transition as the shift from Basic-Level Semantic Intelligence (BLSI) to High-Level Semantic Intelligence (HLSI). However, this issue has not yet been systematically and comprehensively examined in prior work. Motivated by this gap, this survey reviews the development of AI semantic intelligence from the perspective of semantic complexity. We systematically survey existing research on HLS tasks, including humor, sarcasm, metaphor, empathy, persuasion, narrative, and other general HLS phenomena, across text, speech, vision, and multimodal scenarios. Specifically, we summarize data construction methods, modeling and optimization strategies, and evaluation methodologies for both understanding and generation. HLS is essential for advancing AI toward genuinely human-like intelligence. By synthesizing existing methods and insights from the perspective of semantic intelligence, this survey aims to support the continued development of AI toward HLSI.
