Zero-shot retrieval refers to the ability of a system to retrieve relevant information for a query without having seen the query or associated data during training. This is typically achieved using transfer learning or pre-trained models that have generalized knowledge from other domains or tasks.
In zero-shot retrieval, the system can leverage embeddings or semantic representations to match queries to documents that share similar meaning, even if the system has never encountered the exact terms in the query. For example, using a pre-trained language model, a search engine might be able to retrieve relevant results for a completely new query it has never seen before.
Zero-shot retrieval is useful in applications where training data for every possible query is not feasible, such as with large, dynamic datasets or rapidly changing domains.