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Contrastive learning boosts authorship verification accuracy to 98 percent

Contrastive Learning for Authorship Verification

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.

Wed 23 SeptComputation and LanguageMachine Learning

The gist

Determining if two texts come from the same author can be tricky. The authors show that a method called contrastive learning works better than traditional classification approaches for this task. They found that several factors like the training setup and data preparation influence performance. Their final model, based on a version of BERT, reached very high accuracy on a standard authorship test.

Open → 2609.28471v1