Large Language Models and Future of Information Retrieval: Opportunities and Challenges

Recent years have seen great success of large language models (LLMs) in performing many natural language processing tasks with impressive performance, including tasks that directly serve users such as question answering and text summarization. They open up unprecedented opportunities for transforming information retrieval (IR) research and applications. However, concerns such as halluciation undermine their trustworthiness, limiting their actual utility when deployed in real-world applications, especially highstake applications where trust is vital. How can we both exploit the strengths of LLMs and mitigate any risk caused by their weaknesses when applying LLMs to IR? What are the best opportunities for us to apply LLMs to IR? What are the major challenges that we will need to address in the future to fully exploit such opportunities? Given the anticipated growth of LLMs, what will future information retrieval systems look like? Will LLMs eventually replace an IR system? In this perspective paper, we examine these questions and provide provisional answers to them. We argue that LLMs will not be able to replace search engines, and future LLMs would need to learn how to use a search engine so that they can interact with a search engine on behalf of users. We conclude with a set of promising future research directions in applying LLMs to IR.