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Contrastive Learning for Authorship Verification

2026-09-23 17:59:08
Peter Kirby
arXiv_CL
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2609.28471

PDF

https://arxiv.org/pdf/2609.28471.pdf

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