Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Huawei Technologies Ltd. Co（华为技术有限公司）；Jilin University（吉林大学）；Zhejiang University（浙江大学）；Shandong University（山东大学）；Shandong Artificial Intelligence Institute（山东人工智能研究院）；South China University of Technology（华南理工大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Wuhan University（武汉大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanxi University（山西大学）；Jiangxi Normal University（江西师范大学）；Hefei University of Technology（合肥工业大学）；Baidu Inc.（百度公司）；Tianjin University（天津大学）；Shanghai Jiao Tong University（上海交通大学）；Chongqing University of Technology（重庆理工大学）
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop's outcomes, including the rethinking of IR's core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
Multimodal Art Projection Research Community（多模态艺术投影研究社区）；HKUST（香港科技大学）；Fudan University（复旦大学）；Peking University（北京大学）；Shanghai Jiaotong University（上海交通大学）；Nanjing University（南京大学）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）
ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artifical Intelligence, Nanjing University（南京大学人工智能学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
StratMem-Bench: Evaluating Strategic Memory Use in Virtual Character Conversation Beyond Factual Recall
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院，中国）；Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用国家重点实验室（东南大学），中华人民共和国教育部，中国）；College of Design and Innovation, Tongji University, China（同济大学设计与创新学院，中国）
Using Artificial Intuition in Distinct, Minimalist Classification of Scientific Abstracts for Management of Technology Portfolios
Information Sciences Institute, Viterbi School of Engineering, University of Southern California（信息科学研究所，工程学院，南加州大学）；Sol Price School of Public Policy, University of Southern California（索尔价格公共政策学院，南加州大学）
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯人工智能实验室）
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary
Guangdong University of Technology（广东工业大学）；East China Jiaotong University（华东交通大学）；Emory University（埃默里大学）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；South China University of Technology（华南理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Amazon（亚马逊公司）；University of Toronto（多伦多大学）
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Guilin University of Electronic Technology（桂林电子科技大学）；Shanghai Jiao Tong University（上海交通大学）
Kuaishou Technology（快手科技）；University of Rochester（罗切斯特大学）；University of Washington（华盛顿大学）
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the second phase, these predictions are aggregated according to a set of 'strategic parameters' to meet a diverse set of business goals, such as longer user engagement, higher revenue potential, or more community/network interactions. In addition to building accurate predictive models, it is also crucial to optimize this set of 'strategic parameters' so that primary goals are optimized while secondary guardrails are not hu…
