@inproceedings{kasa-etal-2025-generative,
    title = "Generative or Discriminative? Revisiting Text Classification in the Era of Transformers",
    author = "Kasa, Siva Rajesh  and
      Gupta, Karan  and
      Roychowdhury, Sumegh  and
      Kumar, Ashutosh  and
      Biruduraju, Yaswanth  and
      Kasa, Santhosh Kumar  and
      Priyatam, Pattisapu Nikhil  and
      Bhattacharya, Arindam  and
      Agarwal, Shailendra  and
      Huddar, Vijay",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.486/",
    pages = "9615--9637",
    ISBN = "979-8-89176-332-6",
    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.*"
}