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.Comment: Accepted at EACL 2023; camera-ready versio