From Queries to Understanding: Designing the Next-Generation Search Engine

Imagine typing a question into a search engine and getting not just a list of blue links, but a thoughtful, accurate answer that understands exactly what you need. From queries to understanding, the way we access and interpret information has been transformed. This paper explores the evolution of intelligent information retrieval systems (IRS), particularly in the context of large language models (LLMs) and their implications for future IR research and applications. We aim to address critical questions regarding the strengths and weaknesses of LLMs in enhancing IR systems. We discuss traditional IR methods, their limitations, and the rise of LLMs, emphasizing the need for a balanced approach that combines retrieval and generation techniques to improve user experience and satisfaction. The paper proposes a framework for future research that leverages LLMs to create intelligent, transparent, and responsible information retrieval system (IRS).