Generative or Discriminative? Revisiting Text Classification in the Era of Transformers
Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya
Abstract
*The comparison between discriminative and generative classifiers has intrigued researchers since [Efron (1975)’s](https://www.jstor.org/stable/2285453) 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, 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.*
BibTeX
@inproceedings{emnlp2025_generativeordisc,
title = {Generative or Discriminative? Revisiting Text Classification in the Era of Transformers},
author = {Siva Rajesh Kasa and Karan Gupta and Sumegh Roychowdhury and Ashutosh Kumar and Yaswanth Biruduraju and Santhosh Kumar Kasa and Pattisapu Nikhil Priyatam and Arindam Bhattacharya and Shailendra Agarwal and Vijay Huddar},
booktitle = {EMNLP 2025},
year = {2025}
}