Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models

TL;DR

This paper explores how Transformers process sequences by analyzing their layerwise behavior, revealing that lower layers extract features while upper layers achieve feature decoupling, which explains their multi-task learning capabilities.

Contribution

It provides the first detailed layerwise analysis of Transformers on Hidden Markov Models, combining empirical observations with theoretical insights into their expressiveness.

Findings

Lower layers focus on neighboring token features

Upper layers exhibit high time disentanglement

Theoretical analysis supports empirical observations

Abstract

Transformer based models have shown remarkable capabilities in sequence learning across a wide range of tasks, often performing well on specific task by leveraging input-output examples. Despite their empirical success, a comprehensive theoretical understanding of this phenomenon remains limited. In this work, we investigate the layerwise behavior of Transformers to uncover the mechanisms underlying their multi-task generalization ability. Taking explorations on a typical sequence model, i.e, Hidden Markov Models, which are fundamental to many language tasks, we observe that: first, lower layers of Transformers focus on extracting feature representations, primarily influenced by neighboring tokens; second, on the upper layers, features become decoupled, exhibiting a high degree of time disentanglement. Building on these empirical insights, we provide theoretical analysis for the expressiveness power of Transformers. Our explicit constructions align closely with empirical observations, providing theoretical support for the Transformer's effectiveness and efficiency on sequence learning across diverse tasks.