Papers with generative classifiers
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
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Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference (2020.emnlp-main)
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| Challenge: | Recent work has shown advantages of generative classifiers in terms of data efficiency and robustness. |
| Approach: | They propose a generative classifier for natural language inference (NLI) they compare it to discriminative models and large-scale pretrained models like BERT . |
| Outcome: | The proposed classifier outperforms discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise. |