There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Current synthesized data augmentation methods for QA are hampered by increased training costs. To address this issue, we propose a novel approach that combines prompting methods and linear probing then fine-tuning strategy, which does not entail additional cost. Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models. Our approach outperforms state-of-the-art baselines, with an average increase in F1 score of 4.5%-7.9%. Furthermore, our method can be easily integrated into any pre-trained models and offers a promising solution to the under-explored cross-domain QA task. We release our source code at GitHub*.

翻译：近年来，自然语言处理（NLP）模型在领域外泛化能力方面引发了越来越多的关注，尤其是在问答（QA）任务中。当前用于QA的合成数据增强方法受到训练成本增加的制约。为了解决这一问题，我们提出了一种结合提示方法和线性探测再微调策略的新方法，该方法不会带来额外成本。我们的方法在理论和实证上均被证明能有效提升生成式模型和判别式模型的泛化能力。该方法在所有基线模型上均取得最优性能，F1分数平均提升4.5%至7.9%。此外，我们的方法可轻松集成到任何预训练模型中，为尚未充分探索的跨域问答任务提供了一种有前景的解决方案。我们在GitHub*上发布了源代码。