Difference Between Autoregressive And Non-Autoregressive Models

In the field of machine learning and natural language processing, understanding the distinction between autoregressive and non-autoregressive models is crucial for selecting the appropriate approach for various tasks. This document explores the fundamental differences between these two types of models, highlighting their architectures, applications, and performance characteristics.

Introduction

Two popular types of models, autoregressive and non-autoregressive, play a major role in these tasks. If you’re a beginner and have no idea what these terms mean, don’t worry! This guide will explain them in simple terms, breaking down the differences, where they’re used, and why they matter. By the end of this blog, you’ll understand these concepts and how they shape many modern AI applications.

What Are Autoregressive Models?

Let’s start with the term autoregressive. It may sound complicated, but it’s simple when broken down:

1. “Auto” means self.
2. “Regressive” means it uses past data to make predictions.

Key Concept:

Autoregressive models generate outputs sequentially, where each output is conditioned on the previous outputs. This…