Large language model powered agents for information retrieval

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 supp...

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Main Authors: ZHANG, An, DENG, Yang, LIN, Yankai, CHEN, Xu, WEN, Ji-Rong, CHUA, Tat-Seng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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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
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-101072024-08-01T15:02:59Z Large language model powered agents for information retrieval ZHANG, An DENG, Yang LIN, Yankai CHEN, Xu WEN, Ji-Rong CHUA, Tat-Seng 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. 2024-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9104 info:doi/10.1145/3626772.3661375 https://ink.library.smu.edu.sg/context/sis_research/article/10107/viewcontent/3626772.3661375.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Large Language Model Social Network Recommendation Conversational Agent Databases and Information Systems Programming Languages and Compilers
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Large Language Model
Social Network
Recommendation
Conversational Agent
Databases and Information Systems
Programming Languages and Compilers
spellingShingle Large Language Model
Social Network
Recommendation
Conversational Agent
Databases and Information Systems
Programming Languages and Compilers
ZHANG, An
DENG, Yang
LIN, Yankai
CHEN, Xu
WEN, Ji-Rong
CHUA, Tat-Seng
Large language model powered agents for information retrieval
description 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.
format text
author ZHANG, An
DENG, Yang
LIN, Yankai
CHEN, Xu
WEN, Ji-Rong
CHUA, Tat-Seng
author_facet ZHANG, An
DENG, Yang
LIN, Yankai
CHEN, Xu
WEN, Ji-Rong
CHUA, Tat-Seng
author_sort ZHANG, An
title Large language model powered agents for information retrieval
title_short Large language model powered agents for information retrieval
title_full Large language model powered agents for information retrieval
title_fullStr Large language model powered agents for information retrieval
title_full_unstemmed Large language model powered agents for information retrieval
title_sort large language model powered agents for information retrieval
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9104
https://ink.library.smu.edu.sg/context/sis_research/article/10107/viewcontent/3626772.3661375.pdf
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