Information Retrieval for Artificial General Intelligence: A New Perspective of Information Retrieval Research

Abstract

Traditionally, the users of an information retrieval (IR) system have been human users. We present a new perspective on IR research in which the users of an IR system are intelligent agents instead of human users. Extending the current work on retrieval-augmented generation (RAG), we identify five novel IR tasks that an intelligent agent must be able to perform in order to achieve Human-Level Artificial Intelligence, or Artificial General Intelligence (AGI), including 1) External Information Retrieval (EIR) to access new information unseen by the agent, 2) Provenance Information Retrieval (PIR) to trace the provenance of information, 3) Curriculum Information Retrieval (CIR) to actively acquire the most useful new data and information for lifelong learning, 4) Rule Information Retrieval (RIR) to perform reasoning and problem solving, and 5) Scenario Information Retrieval (SIR) to leverage past scenarios for problem solving and decision making. We compare these new IR tasks with the traditional IR tasks performed by an IR system that serves human users and systematically examine the challenges involved in the five new IR tasks, providing a roadmap for new IR research within the broader context of AGI development.