@inproceedings{saad-falcon-etal-2023-udapdr,
    title = "{UDAPDR}: Unsupervised Domain Adaptation via {LLM} Prompting and Distillation of Rerankers",
    author = "Saad-Falcon, Jon  and
      Khattab, Omar  and
      Santhanam, Keshav  and
      Florian, Radu  and
      Franz, Martin  and
      Roukos, Salim  and
      Sil, Avirup  and
      Sultan, Md  and
      Potts, Christopher",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.emnlp-main.693/",
    doi = "10.18653/v1/2023.emnlp-main.693",
    pages = "11265--11279",
    abstract = "Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply. The method begins by generating a small number of synthetic queries using an expensive LLM. After that, a much less expensive one is used to create large numbers of synthetic queries, which are used to fine-tune a family of reranker models. These rerankers are then distilled into a single efficient retriever for use in the target domain. We show that this technique boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods."
}