Query Generation

Query generation focuses on automatically creating effective search queries from various inputs, such as natural language descriptions of information needs or example documents, to improve information retrieval efficiency and effectiveness. Current research emphasizes improving query relevance and diversity through techniques like retrieval-augmented generation, neurosymbolic reasoning, and ensemble methods, often leveraging large language models (LLMs) and incorporating user feedback for iterative refinement. These advancements have significant implications for various applications, including cross-lingual information retrieval, clinical cohort discovery, and personalized recommendation systems, by enabling more efficient and accurate access to information.