Paper recorded by Signals 4 on 2026-09-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24
Category: cs.LG · 机器学习 · first seen 2026-09-24
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 a