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

• URL: http://arxiv.org/abs/2303.00807v3
• Date: Fri, 13 Oct 2023 17:23:04 GMT
• Title: UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
• Authors: Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Arafat Sultan, Christopher Potts
• Abstract summary: We develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply. We show that this technique boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods.
• Score: 42.84866455416052
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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