Iterative Retrieval

Iterative retrieval enhances information retrieval by repeatedly refining search queries and retrieving additional relevant information, improving performance on complex tasks like multi-hop question answering and open-domain summarization. Current research focuses on developing efficient iterative retrieval methods, often employing reinforcement learning to optimize retrieval policies and leveraging large language models for query generation and relevance assessment. These advancements aim to overcome limitations of single-stage retrieval, leading to more accurate and comprehensive results in various applications, including question answering systems and code completion tools. The resulting improvements in information access and processing have significant implications for diverse fields requiring complex information synthesis.