Hybrid Search

Hybrid search techniques combine different search algorithms or models to leverage their individual strengths and overcome limitations, aiming for improved accuracy, efficiency, or completeness in diverse applications. Current research explores hybrid approaches incorporating techniques like large language models, embedding models, and various optimization strategies, often focusing on specific domains (e.g., legal text retrieval, image processing) or problem types (e.g., planning, Bayesian optimization). These advancements offer significant potential for enhancing the performance of search systems across various fields, from information retrieval and planning to image processing and project management, by providing more robust and efficient solutions.