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

Understand

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.