Synthetic Query Generation

Synthetic query generation leverages large language models (LLMs) to create artificial search queries, addressing data scarcity and improving the performance of information retrieval (IR) systems and related tasks like virtual assistants and dialogue systems. Current research focuses on generating queries that are both realistic and diverse, often employing techniques like parameter-efficient fine-tuning and regularization to enhance quality and reduce overfitting. This approach holds significant promise for advancing various applications by augmenting training data for downstream tasks, improving model robustness, and enabling privacy-preserving training methodologies.