Synthetic Query

Synthetic query generation leverages large language models (LLMs) to create artificial queries for various information retrieval tasks, aiming to overcome limitations of scarce or expensive labeled data. Current research focuses on optimizing LLM prompts for generating high-quality synthetic queries, improving retrieval performance through techniques like multi-query instructions and semi-supervised learning, and evaluating the effectiveness of these synthetic datasets in diverse applications such as question answering and spreadsheet manipulation. This approach holds significant promise for advancing information retrieval, particularly in low-resource scenarios and for improving the efficiency and robustness of LLMs in real-world applications.