基于对比学习的作者身份验证方法在 PAN21 上达到 98.4% 准确率
Contrastive Learning for Authorship Verification
研究者用对比学习训练了一个 ModernBERT Bi-Encoder，判断两段文字是不是同一人写的，在 PAN21 上准确率 98.4%，还列出了调参要点。
一项研究比较了对比学习与分类方法在作者身份验证任务上的表现，结果显示对比学习效果更好。研究指出损失函数、batch size、训练时长、预训练模型、输入上下文长度和随机文本片段数据增强是影响性能的关键因素。基于这些发现，团队构建了 ModernBERT Bi-Encoder 模型，在 PAN21 authorship verification 任务上取得 98.4% 的准确率。
Contrastive Learning for Authorship Verification
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.