  Deploying dense retrieval models efficiently is becoming increasingly
important across various industries. This is especially true for enterprise
search services, where customizing search engines to meet the time demands of
different enterprises in different domains is crucial. Motivated by this, we
develop a time-efficient approach called DREditor to edit the matching rule of
an off-the-shelf dense retrieval model to suit a specific domain. This is
achieved by directly calibrating the output embeddings of the model using an
efficient and effective linear mapping. This mapping is powered by an edit
operator that is obtained by solving a specially constructed least squares
problem. Compared to implicit rule modification via long-time finetuning, our
experimental results show that DREditor provides significant advantages on
different domain-specific datasets, dataset sources, retrieval models, and
computing devices. It consistently enhances time efficiency by 100-300 times
while maintaining comparable or even superior retrieval performance. In a
broader context, we take the first step to introduce a novel embedding
calibration approach for the retrieval task, filling the technical blank in the
current field of embedding calibration. This approach also paves the way for
building domain-specific dense retrieval models efficiently and inexpensively.
