2026年PAN竞赛中的DACTYL团队：用于人工智能文本检测的贝叶斯数据混合与经验性X风险最小化
Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection
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中文总结 AI 辅助
该研究针对人工智能文本检测中分类器分布外性能不佳问题进行探索，核心方法是用贝叶斯分类头微调模型并选择合并训练集，训练多个分类器，主要贡献是通过精心管理数据集提升了分布外性能，且发布了相关模型。
中文摘要 AI 辅助
现有研究表明，人工智能生成文本检测分类器在分布内文本上表现出色，但在分布外文本上性能不佳，存在过拟合特定数据集特征的问题。为解决这一问题，该团队用贝叶斯分类头微调BERT-tiny模型，从三个不同数据集选择文本作为合并训练集。训练了三个分类器，在PAN 2026测试集上，DeBERTa-V3-large平均得分为0.882，ModernBERT-large得分为0.96，MCGrad得分最佳，平均分为0.974。结果表明精心的数据集管理可带来强大的分布外性能，团队还发布了相关模型。
英文摘要
Existing research shows that AI-generated text detection classifiers achieve strong in-distribution (ID) performance but do not maintain the same performance on out-of-distribution (OOD) texts, suggesting overfitting to dataset-specific features. However, combining different training datasets doesn't always improve performance and, in some cases, can even encourage shortcut learning. To address this issue, we fine-tune BERT-tiny models with Bayesian classification heads to select texts across three different datasets to use as a consolidated training set. We trained three different classifiers: fine-tuned DeBERTa-V3-large and ModernBERT-large classifiers via empirical X-risk minimization, and an MCGrad model that calibrates the predictions from the ModernBERT-large classifier. The DeBERTa-V3-large-large classifier achieves a mean score of 0.882 on the PAN 2026 test set across five metrics: AUROC, $F_1$, C@1, Brier score, and $F_{0.5u}$. ModernBERT-large achieves a score of 0.96 while MCGrad achieves the best score of the three with a mean score of 0.974, ranking second on the leaderboard. Our results highlight that careful dataset curation can lead to strong OOD performance. We release our ModernBERT-large and DeBERTa-V3-large models at https://huggingface.co/collections/ShantanuT01/panclef-2026 .
