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arXiv · 2609.28471

Contrastive Learning for Authorship Verification

Peter Kirby
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Abstract

Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.

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Peter Kirby. 2026-09-23. Contrastive Learning for Authorship Verification. https://doi.org/10.1007/978-3-032-39150-6_7

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