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Many information retrieval tasks require large labeled datasets forfine-tuning. However, such datasets are often unavailable, and their utilityfor real-world applications can diminish quickly due to domain shifts. Toaddress this challenge, we develop and motivate a method for using largelanguage models (LLMs) to generate large numbers of synthetic queries cheaply.The method begins by generating a small number of synthetic queries using anexpensive LLM. After that, a much less expensive one is used to create largenumbers of synthetic queries, which are used to fine-tune a family of rerankermodels. These rerankers are then distilled into a single efficient retrieverfor use in the target domain. We show that this technique boosts zero-shotaccuracy in long-tail domains and achieves substantially lower latency thanstandard reranking methods.

UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers