Large Language Models Empowered Personalized Web Agents
National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Eastern Institute of Technology（宁波东方理工大学）
Web agents have emerged as a promising direction to automate Web task completion based on user instructions, significantly enhancing user experience. Recently, Web agents have evolved from traditional agents to Large Language Models (LLMs)-based Web agents. Despite their success, existing LLM-based Web agents overlook the importance of personalized data (e.g., user profiles and historical Web behaviors) in assisting the understanding of users' personalized instructions and executing customized actions. To overcome the limitation, we first formulate the task of LLM-empowered personalized Web agents, which integrate personalized data and user instructions to personalize instruction comprehension and action execution. To address the absence of a comprehensive evaluation benchmark, we construct a Personalized Web Agent Benchmark (PersonalWAB), featuring user instructions, personalized user data, Web functions, and two evaluation paradigms across three personalized Web tasks. Moreover, we propose a Personalized User Memory-enhanced Alignment (PUMA) framework to adapt LLMs to the personalized Web agent task. PUMA utilizes a memory bank with a task-specific retrieval strategy to filter relevant historical Web behaviors. Based on the behaviors, PUMA then aligns LLMs for personalized action execution through fine-tuning and direct preference optimization. Extensive experiments validate the superiority of PUMA over existing Web agents on PersonalWAB.
Personalized Graph-Empowered Large Language Model for Proactive Information Access
Department of Computer Science（计算机科学系）；Information Engineering National Taiwan University Taipei, Taiwan（信息工程国立台湾大学台北市）；Department of Computer Science National Yang Ming Chiao Tung University Hsinchu, Taiwan（计算机科学国立阳明交通大学新竹市）；Institute of Information Science Academia Sinica Taipei, Taiwan（信息科学研究所台湾学术院台北市）；Information Engineering AI Research Center (AINTU) National Taiwan University Taipei, Taiwan（信息工程人工智能研究中心（AINTU）国立台湾大学台北市）
Large AI Model Empowered Multimodal Semantic Communications
Hunan Normal University（湖南师范大学）；Hunan University of Technology and Business（湖南工商大学）；Brunel University（布鲁内尔大学）；Nanjing University（南京大学）；Southeast University（东南大学）
An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem
College of Intelligence and Computing（智能与计算学院）；Tianjin University（天津大学）；Tianjin Key Laboratory of Healhy Habitat and Smart Technology（天津健康人居环境与智能技术重点实验室）；Laboratory of Computation and Analytics of Complex Management Systems（复杂管理系统计算与分析实验室）；Faculty of Environment, Science and Economy（环境、科学与经济学院）
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
Qatar University（卡塔尔大学）；Birmingham City University（伯明翰城市大学）；Hamad Bin Khalifa University (HBKU)（哈马德·本·哈利法大学）；University of the West of England (UWE)（西英格兰大学）
Artificial Intelligence Application Research Center, Huawei Technologies（华为技术有限公司人工智能应用研究中心）
Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents. In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking list. Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly.
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
Microsoft Research（微软研究院）；Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属上海市精神卫生中心老年精神科）
This paper provides an overall presentation of the M-PIRO project. M-PIRO is developing technology that will allow museums to generate automatically textual or spoken descriptions of exhibits for collections available over the Web or in virtual reality environments. The descriptions are generated in several languages from information in a language-independent database and small fragments of text, and they can be tailored according to the backgrounds of the users, their ages, and their previous interaction with the system. An authoring tool allows museum curators to update the system's database…
Generate, Not Recommend: Personalized Multimodal Content Generation
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学系）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学agate智能学院）；Serendipity One Inc.（Serendipity One公司）
University of Southern California（南加州大学）；Intel Labs（英特尔实验室）
Continual learning systems will interact with humans, with each other, and with the physical world through time -- and continue to learn and adapt as they do. An important open problem for continual learning is a large-scale benchmark that enables realistic evaluation of algorithms. In this paper, we study a natural setting for continual learning on a massive scale. We introduce the problem of personalized online language learning (POLL), which involves fitting personalized language models to a population of users that evolves over time. To facilitate research on POLL, we collect massive datas…
