Search Still Matters: Information Retrieval in the Era of Generative AI
It addresses the role of generative AI in information retrieval for academics, but it is incremental as it reaffirms existing needs without introducing new methods.
The paper examines how generative AI and large language models fit into information retrieval processes, particularly for academic users, concluding that search systems remain essential despite AI advancements.
Objective: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? Process: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems. Conclusions: There are many information needs, from simple to complex, that motivate use of IR. Users of such systems, particularly academics, have concerns for authoritativeness, timeliness, and contextualization of search. While LLMs may provide functionality that aids the IR process, the continued need for search systems, and research into their improvement, remains essential.