Agentic Information Retrieval

• URL: http://arxiv.org/abs/2410.09713v2
• Date: Tue, 29 Oct 2024 13:19:12 GMT
• Title: Agentic Information Retrieval
• Authors: Weinan Zhang, Junwei Liao, Ning Li, Kounianhua Du,
• Abstract summary: We introduce Agentic Information Retrieval (Agentic IR), a novel IR paradigm shaped by the capabilities of large language models (LLMs) We discuss three types of cutting-edge applications of agentic IR and the challenges faced.
• Score: 21.741669515186146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: What will information entry look like in the next generation of digital products? Since the 1970s, user access to relevant information has relied on domain-specific architectures of information retrieval (IR). Over the past two decades, the advent of modern IR systems, including web search engines and personalized recommender systems, has greatly improved the efficiency of retrieving relevant information from vast data corpora. However, the core paradigm of these IR systems remains largely unchanged, relying on filtering a predefined set of candidate items. Since 2022, breakthroughs in large language models (LLMs) have begun transforming how information is accessed, establishing a new technical paradigm. In this position paper, we introduce Agentic Information Retrieval (Agentic IR), a novel IR paradigm shaped by the capabilities of LLM agents. Agentic IR expands the scope of accessible tasks and leverages a suite of new techniques to redefine information retrieval. We discuss three types of cutting-edge applications of agentic IR and the challenges faced. We propose that agentic IR holds promise for generating innovative applications, potentially becoming a central information entry point in future digital ecosystems.

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