Large language model powered agents in the web

• Main Authors: DENG, Yang, ZHANG, An, 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/9105; https://ink.library.smu.edu.sg/context/sis_research/article/10108/viewcontent/3589335.3641240.pdf
• Institution: Singapore Management University

Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. 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 web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as web mining, social networks, recommender systems, and conversational systems. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way we interact with web platforms.