Large language model powered agents for information retrieval

• Main Authors: ZHANG, An, DENG, Yang, LIN, Yankai, CHEN, Xu, WEN, Ji-Rong, CHUA, Tat-Seng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Large Language Model; Social Network; Recommendation; Conversational Agent; Databases and Information Systems; Programming Languages and Compilers
• Online Access: https://ink.library.smu.edu.sg/sis_research/9104; https://ink.library.smu.edu.sg/context/sis_research/article/10107/viewcontent/3626772.3661375.pdf
• Institution: Singapore Management University

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various information retrieval fields, such as search engines, social networks, recommender systems, and conversational assistants. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way of information retrieval.