Generating knowledge-intensive and comprehensive long texts, such as encyclopedia articles, remains significant challenges for Large Language Models. It requires not only the precise integration of facts but also the maintenance of thematic coherence throughout the article. Existing methods, such as direct generation and multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency. To address these challenges, we propose RAPID, an efficient retrieval-augmented long text generation framework. RAPID consists of three main modules: (1) Retrieval-augmented preliminary outline generation to reduce hallucinations, (2) Attribute-constrained search for efficient information discovery, (3) Plan-guided article generation for enhanced coherence. Extensive experiments on our newly compiled benchmark dataset, FreshWiki-2024, demonstrate that RAPID significantly outperforms state-of-the-art methods across a wide range of evaluation metrics (e.g. long-text generation, outline quality, latency, etc). Our work provides a robust and efficient solution to the challenges of automated long-text generation.
Content Planning for Neural Story Generation with Aristotelian Rescoring
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of Edinburgh（爱丁堡大学）；Columbia University（哥伦比亚大学）；University of California Los Angeles（加州大学洛杉矶分校）
CoAuthorAI: A Human in the Loop System For Scientific Book Writing
Kexin Technology, Beijing（凯欣科技，北京）；Institute for Sustainable Industries and Liveable Cities, Victoria University（可持续产业与宜居城市研究所，维多利亚大学）；School of Information Technology, Deakin University（信息科技学院，德坎大学）
Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning
School of Mechanical, Aerospace, and Manufacturing Engineering University of Connecticut Storrs, Connecticut, USA（机械、航空航天与制造工程学院 美国康涅狄格大学 斯托尔斯分校）；The United States Army Combat Capabilities Development Command Ground Vehicle Systems Center Warren, Michigan, USA（美国陆军作战能力发展司令部地面车辆系统中心 华伦，密歇根州，美国）
LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
Department of Civil & Environmental Engineering, University of Maryland, College Park（大学公园马里兰大学土木与环境工程系）；Center for Urban Science and Progress, New York University（纽约大学城市科学与进步中心）；Department of Computer Science, University of Alabama（阿拉巴马大学计算机科学系）；School of Information, University of South Florida（佛罗里达州立大学信息学院）
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Zhejiang University（浙江大学）；ZJU-Ant Group Joint Research Center for Knowledge Graphs（浙江大学-蚂蚁集团知识图谱联合研究中心）；Ant Group（蚂蚁集团）；National University of Singapore（新加坡国立大学）
ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
Department of Computer Science（计算机科学系）；San Francisco State University（旧金山州立大学）；Department of Mechanical & Industrial Engineering（机械与工业工程系）；Louisiana State University（路易斯安那州立大学）
Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning
Department of Psychology, University of Toronto（多伦多大学心理学系）；Vector Institute（向量研究所）；Schwartz Reisman Institute of Technology & Society, University of Toronto（多伦多大学科技与社会研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation
The Hong Kong Polytechnic University（香港理工大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；Great Bay University（大湾区大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Strategy-Augmented Planning for Large Language Models via Opponent Exploitation
Nanjing University of Information Science & Technology（南京信息工程大学）；Nanjing Artificial Intelligence Research of IA（南京人工智能研究所）；University of Chinese Academy of Sciences（中国科学院大学）；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）
LLM-Augmented Chemical Synthesis and Design Decision Programs
École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院洛桑校区）；National Centre of Competence in Research (NCCR) Catalysis（催化领域竞争力研究国立中心）；Harvard University（哈佛大学）；Cornell University（康奈尔大学）
