Can you adapt retrieval models without accessing target data?

Dense retrieval models require labeled query-document pairs to adapt to new domains. In many enterprise contexts, the target collection is unavailable: it may not exist yet, it may be legally restricted (medical records, financial data), or sharing it with a model provider would compromise competitive advantage.

The standard assumption — you need the data to train for the domain — turns out to be false for retrieval. A brief textual description of the target domain is sufficient.

The pipeline: (1) Provide a textual domain description. (2) Use instruction-following LLMs to extract domain properties: document topics, linguistic attributes, source characteristics, terminology patterns. (3) Generate seed documents matching those properties. (4) Iteratively retrieve real-domain-like documents using the seed as query anchor. (5) Generate synthetic queries for the constructed collection. (6) Use pseudo-relevance labels to fine-tune the retrieval model.

The retrieval-augmented approach to domain understanding is key: at step (2), the domain description itself becomes a RAG query to extract structured properties, which are then used to parameterize generation at step (3). Bootstrapping from description through synthesis to training.

Evaluation on five diverse target domains shows that description-based adaptation outperforms existing dense retrieval baselines in the zero-target-access scenario. The approach enables adaptation in precisely the contexts where conventional adaptation is blocked: privacy-sensitive domains, legally restricted data, competitive scenarios.