Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the task of language modeling. This leads to good in-domain scores but can be impractical for domain- or resource-restricted settings. A solution is to use a related-domain adapter for the novel domain at test time. In this paper, we introduce AdapterSoup, an approach that performs weight-space averaging of adapters trained on different domains. Our approach is embarrassingly parallel: first, we train a set of domain-specific adapters; then, for each novel domain, we determine which adapters should be averaged at test time. We present extensive experiments showing that AdapterSoup consistently improves performance to new domains without extra training. We also explore weight averaging of adapters trained on the same domain with different hyper-parameters, and show that it preserves the performance of a PLM on new domains while obtaining strong in-domain results. We explore various approaches for choosing which adapters to combine, such as text clustering and semantic similarity. We find that using clustering leads to the most competitive results on novel domains.

翻译：预训练语言模型（PLMs）在大规模语料上训练，但通常需要适应特定领域。一种参数高效的适配方法建议针对每个领域在语言建模任务上训练适配器。这能取得良好的领域内得分，但在领域受限或资源受限场景下可能不切实际。一种解决方案是在测试时为新领域使用相关领域的适配器。本文提出AdapterSoup，一种对不同领域训练的适配器进行权重空间平均的方法。该方法具有令人尴尬的并行性：首先，我们训练一组领域特定适配器；然后，针对每个新领域，确定在测试时应平均哪些适配器。我们通过大量实验表明，AdapterSoup无需额外训练即可持续提升新领域的性能。我们还探索了同一领域内使用不同超参数训练的适配器的权重平均，并证明该方法在保持PLM对新领域性能的同时，能获得强大的领域内结果。我们研究了多种选择适配器组合的方案，例如文本聚类和语义相似度。结果发现，使用聚类能在新领域上取得最具竞争力的表现。