Representations as Language: An Information-Theoretic Framework for Interpretability

TL;DR

This paper introduces an information-theoretic framework to interpret neural model representations as a language, enabling analysis of their structure, training phases, and generalization capabilities.

Contribution

It proposes novel, efficient measures grounded in linguistic theory to quantify and predict the structure and generalization of model representations.

Findings

Representations evolve through two training phases: in-distribution learning and noise robustness.

Larger models compress their representations more than smaller ones.

The measures can predict which models will generalize better.

Abstract

Large scale neural models show impressive performance across a wide array of linguistic tasks. Despite this they remain, largely, black-boxes - inducing vector-representations of their input that prove difficult to interpret. This limits our ability to understand what they learn, and when the learn it, or describe what kinds of representations generalise well out of distribution. To address this we introduce a novel approach to interpretability that looks at the mapping a model learns from sentences to representations as a kind of language in its own right. In doing so we introduce a set of information-theoretic measures that quantify how structured a model's representations are with respect to its input, and when during training that structure arises. Our measures are fast to compute, grounded in linguistic theory, and can predict which models will generalise best based on their representations. We use these measures to describe two distinct phases of training a transformer: an initial phase of in-distribution learning which reduces task loss, then a second stage where representations becoming robust to noise. Generalisation performance begins to increase during this second phase, drawing a link between generalisation and robustness to noise. Finally we look at how model size affects the structure of the representational space, showing that larger models ultimately compress their representations more than their smaller counterparts.