Extending Neural Networks to New Lengths: Enhancing Symbol Processing and Generalization

Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory

Table of Content

• Introduction to the Length Extrapolation Problem

• Why Do Neural Networks Struggle with Length-Extraploation?

• Core Idea: Modeling Pointers to Learn the Symbolic Rules

  • Design Principles

  • How Explicit Pointers Power Memory Manipulation and Generalization

• Modeling Explicit Pointers in Neural Networks

• Understanding Pointer-Augmented Neural Memory (PANM)

  • Pointer Unit Operations

  • Two Modes of Memory Access

  • The Controller: Integrating Mode 1 and Mode 2 Access

• Notable Empirical Results

• Appendix

Introduction to the Length Extrapolation Problem

Length extrapolation in ML/AI refers to the ability of a model to predict outputs for sequences that are significantly longer (or shorter) than those it was trained on. This is a common challenge AI models face, particularly in tasks involving sequential data like natural language processing or time series analysis.

Many deep sequence learning models struggle to generalize to longer or more complex sequences than those encountered during training. In other words, they perform well on sequences of similar length to the training data but fail catastrophically when predicting longer sequences. This “extrapolation” problem remains one of the few unresolved challenges in modern AI. Read the full article