Exploring ChatGPT for next-generation information retrieval: Opportunities and challenges

Article type: Research Article

Authors: Huang, Yizheng | Huang, Jimmy X.^{; *}

Affiliations: Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Canada

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The rapid advancement of artificial intelligence (AI) has spotlighted ChatGPT as a key technology in the realm of information retrieval (IR). Unlike its predecessors, it offers notable advantages that have captured the interest of both industry and academia. While some consider ChatGPT to be a revolutionary innovation, others believe its success stems from smart product and market strategy integration. The advent of ChatGPT and GPT-4 has ushered in a new era of Generative AI, producing content that diverges from training examples, and surpassing the capabilities of OpenAI’s previous GPT-3 model. In contrast to the established supervised learning approach in IR tasks, ChatGPT challenges traditional paradigms, introducing fresh challenges and opportunities in text quality assurance, model bias, and efficiency. This paper aims to explore the influence of ChatGPT on IR tasks, providing insights into its potential future trajectory.

Keywords: Information retrieval, ChatGPT, large language models

DOI: 10.3233/WEB-230363

Journal: Web Intelligence, vol. 22, no. 1, pp. 31-44, 2024