Mapping the Timescale Organization of Neural Language Models

TL;DR

This paper introduces a neuroscience-inspired method to map and analyze the timescale organization of units in neural language models, revealing distinct functional classes and their roles in language processing.

Contribution

It presents a novel, model-free technique for mapping processing timescales in neural networks and uncovers the functional organization of units based on their timescales.

Findings

Long-timescale units track long-range syntactic dependencies.

A small subset of units (less than 15%) have long timescales with unexplored functions.

Distinct roles for 'controller' and 'integrator' units in language processing.

Abstract

In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual information are functionally organized. Therefore, we applied tools developed in neuroscience to map the "processing timescales" of individual units within a word-level LSTM language model. This timescale-mapping method assigned long timescales to units previously found to track long-range syntactic dependencies. Additionally, the mapping revealed a small subset of the network (less than 15% of units) with long timescales and whose function had not previously been explored. We next probed the functional organization of the network by examining the relationship between the processing timescale of units and their network connectivity. We identified two classes of long-timescale units: "controller" units composed a densely interconnected subnetwork and strongly projected to the rest of the network, while "integrator" units showed the longest timescales in the network, and expressed projection profiles closer to the mean projection profile. Ablating integrator and controller units affected model performance at different positions within a sentence, suggesting distinctive functions of these two sets of units. Finally, we tested the generalization of these results to a character-level LSTM model and models with different architectures. In summary, we demonstrated a model-free technique for mapping the timescale organization in recurrent neural networks, and we applied this method to reveal the timescale and functional organization of neural language models.