Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2024
Abstract
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.
Keywords
Large Language Model, Social Network, Recommendation, Conversational Agent
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, July 14-18
First Page
2989
Last Page
2992
ISBN
9798400704314
Identifier
10.1145/3626772.3661375
Publisher
ACM
City or Country
New York
Citation
ZHANG, An; DENG, Yang; LIN, Yankai; CHEN, Xu; WEN, Ji-Rong; and CHUA, Tat-Seng.
Large language model powered agents for information retrieval. (2024). SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, July 14-18. 2989-2992.
Available at: https://ink.library.smu.edu.sg/sis_research/9104
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3626772.3661375