Generative or Discriminative? Revisiting Text Classification in the Era of Transformers

TL;DR

This paper compares generative and discriminative text classifiers in the transformer era, analyzing their performance, efficiency, and robustness to guide practical model selection.

Contribution

It provides the first comprehensive evaluation of modern generative and discriminative transformer-based classifiers across multiple criteria.

Findings

Classical 'two regimes' phenomenon varies across architectures

Generative models show different sample efficiency and robustness

Guidelines for choosing models based on real-world constraints

Abstract

The comparison between discriminative and generative classifiers has intrigued researchers since Efron's seminal analysis of logistic regression versus discriminant analysis. While early theoretical work established that generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, these trade-offs remain unexplored in the transformer era. We present the first comprehensive evaluation of modern generative and discriminative architectures - Auto-regressive modeling, Masked Language Modeling, Discrete Diffusion, and Encoders for text classification. Our study reveals that the classical 'two regimes' phenomenon manifests distinctly across different architectures and training paradigms. Beyond accuracy, we analyze sample efficiency, calibration, noise robustness, and ordinality across diverse scenarios. Our findings offer practical guidance for selecting the most suitable modeling approach based on real-world constraints such as latency and data limitations.