计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250300107-16.doi: 10.11896/jsjkx.250300107

• 人工智能 • 上一篇    下一篇

深度学习位置编码算法综述

杨格儿1,5, 王馨2, 孙伟1, 王新格3, 胡中瑞3, 孟文君3, 张俊强3, 武星辉3, 刘金山4, 闫昱名3   

  1. 1 中科南京软件技术研究院 南京 211100
    2 苏州科技大学 江苏 苏州 215009
    3 北京华电电子商务科技有限公司 北京 100073
    4 北京卫星制造厂有限公司 北京 1000945 中国科学院工业人工智能研究所 南京 211135
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 刘金山(secularbird_feng@163.com)
  • 作者简介:(geer@njis.ac.cn)
  • 基金资助:
    江苏省前沿技术研发计划(BF2024052);成都市技术创新研发项目(2025-YF08-00097-GX)

Survey on Positional Encoding Algorithms in Deep Learning

YANG Geer1,5, WANG Xin2, SUN Wei1, WANG Xinge3, HU Zhongrui3, MENG Wenjun3, ZHANG Junqiang3, WU Xinghui3, LIU Jinshan4, YAN Yuming3   

  1. 1 Nanjing Software Technology Research Institute,Chinese Academy of Sciences,Nanjing 211100,China
    2 Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    3 Beijing Huadian E-commerce Technology Co.,Ltd.,Beijing 100073,China
    4 Beijing Satellite Manufacturing Factory Co.,Ltd.,Beijing 100094,China 5 Institute of AI for Industries,Chinese Academy of Sciences,Nanjing 211135,China
  • Published:2026-06-16 Online:2026-06-12
  • About author:YANG Geer,born in 2000,master.Her main research interests include sequence modeling and representation learning in generative artificial intelligence.
    LIU Jinshan,born in 1979,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.13872M).His main research interests include intelligent manufacturing,production system,digital twin,Internet of Things system,industrial augmented reality and human-robot collaborative assembly.
  • Supported by:
    Frontier Technologies R&D Program of Jiangsu(BF2024052) and Technology Innovation R&D Project of Chengdu(2025-YF08-00097-GX).

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摘要： 在深度学习领域,位置编码(positional encoding)是提升神经网络理解序列结构能力的必要组成部分。尤其在 Transformer 及其衍生架构中,自注意力机制(self-attention)无法主动捕捉序列中的顺序信息,位置编码由此成为有效的解决方案。位置编码领域的研究涉及其理论基础、不同编码策略的设计思路,以及在各类神经网络架构中的具体应用。首先,传统递归神经网络(RNN)和长短时记忆网络(LSTM)隐式地对序列位置进行建模,这一机制的局限性成为 Transformer 显式引入位置编码的重要理论动机。其次,主流位置编码方法被分为绝对位置编码、相对位置编码和基于偏置的位置编码3类:绝对位置编码包括正弦位置编码和可学习的位置嵌入;相对位置编码方法则以 Transformer-XL 和旋转位置编码(RoPE)为代表;基于偏置的方法典型代表为 ALiBi 和 KERPLE。此外,针对超长序列处理任务的最新优化方法,如 NTK-aware RoPE,YaRN 和 CoPE 等,也进行了详细讨论。同时,位置编码的设计会显著影响模型的性能,包括计算效率、外推能力和长程依赖的建模效果。数值稳定性问题与频谱分布优化等前沿技术挑战,也在近年来逐渐受到关注并深入研究。最后,位置编码领域的研究趋势主要体现在大规模序列建模、混合神经网络架构的设计,以及层级化数据结构的有效表达方面。这些趋势提示了未来位置编码方法在序列建模任务中的潜力与发展方向,相关研究将为研究者与工程师提供更加全面且深入的技术选择与理论参考。

关键词: 位置编码, Transformer模型, 自注意力机制, 序列建模, 旋转位置编码

Abstract: In deep learning,positional encoding constitutes a critical component for enhancing neural networks' capabilities in understanding sequence structures.Particularly within Transformers and their variants,positional encoding addresses the inherent limitation of the self-attention mechanism,which lacks the ability to intrinsically capture sequential order.This paper systematically reviews the theoretical foundations of positional encoding,the conceptual design of various encoding strategies,and their applications across diverse neural network architectures.Initially,the paper revisits traditional models such as Recurrent Neural Networks(RNNs) and Long Short-Term Memory networks(LSTMs),discussing their implicit methods of modeling sequence positions and examining the theoretical motivations behind the introduction of explicit positional encoding in Transformers.Subsequently,a detailed exposition is presented on absolute positional encoding strategies-including sinusoidal positional encoding and learnable positional embeddings-relative positional encoding methods such as Transformer-XL and RoPE,bias-based positional encoding methods like ALiBi and KERPLE,and recent optimization techniques tailored for extremely long sequence tasks,notably NTK-aware RoPE,YaRN,and CoPE.Moreover,the paper conducts an in-depth analysis of positional encoding's impact on model performance,encompassing computational efficiency,extrapolation capabilities,and modeling of long-range dependencies.Frontier topics including numerical stability and frequency spectrum optimization are also addressed.Finally,the study summarizes current research trends in positional encoding and outlines its future prospects in areas such as large-scale sequence modeling,hybrid network architectures,and hierarchical data structure modeling.The overarching aim is to provide researchers and practitioners with a comprehensive and detailed reference to facilitate the selection of appropriate positional encoding methods for specific tasks and to foster further advancements in related fields.

Key words: Positional encoding, Transformer models, Self-attention mechanism, Sequence modeling, Rotary positional encoding

中图分类号: 

  • TP183
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