  Several parameter-efficient fine-tuning methods based on adapters have been
proposed as a streamlined approach to incorporate not only a single specialized
knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of
them at once. Recent works such as AdapterSoup propose to mix not all but only
a selective sub-set of domain-specific adapters during inference via model
weight averaging to optimize performance on novel, unseen domains with
excellent computational efficiency. However, the essential generalizability of
this emerging weight-space adapter mixing mechanism on \textit{unseen,
in-domain examples} remains unexplored. Thus, in this study, we conduct a
comprehensive analysis to elucidate the generalizability of domain-specific
adapter mixtures in in-domain evaluation. We also provide investigations into
the inner workings of the mixture of domain-specific adapters by analyzing
their weight signs, yielding critical analysis on the negative correlation
between their fraction of weight sign difference and their mixtures'
generalizability.
