Abstract. Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
Allen Institute for AI（艾伦人工智能研究所）；Yale University（耶鲁大学）；University of Washington（华盛顿大学）；Hebrew University（希伯来大学）；MIT（麻省理工学院）；Northwestern University（西北大学）
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Stony Brook University（石溪大学）；JP Morgan Chase（摩根大通）；Bloomberg（彭博公司）；Preferabli（普雷费布利公司）；University of Pennsylvania（宾夕法尼亚大学）；National Institute of Health（美国国立卫生研究院）；Grammarly（grammarly公司）
From Weak Task Specifications to Scientific Extraction Agents: Optimizing Task Construction
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能全国重点实验室）；CCCC Second Highway Consultants Co., Ltd.（中交第二公路勘察设计研究院有限公司）
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Oxford（牛津大学）
SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Department of Computer Science, Portland State University, Portland, US（波特兰州立大学计算机科学系）；Information Science and Technology College, Dalian Maritime University, Dalian, China（大连海事大学信息科学与技术学院）
Large language model-assisted discovery of cohorts from scientific literature
University Hospital Frankfurt, Goethe University Frankfurt（法兰克福大学医院，法兰克福大学）；Goethe University Frankfurt（法兰克福大学）；The Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）
Towards unearthing neglected climate innovations from scientific literature using Large Language Models
Grantham Institute for Climate Change and the Environment, Imperial College London（帝国理工学院格兰瑟姆气候变化与环境研究所）；Dyson School of Engineering, Imperial College London（帝国理工学院戴森工程学院）；Department of Earth Science and Engineering, Imperial College London（帝国理工学院地球科学与工程系）
SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model
Bosch Research North America（博世北美研究院）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Hong Kong University of Science and Technology（香港科技大学）
FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration
University of Chinese Academy of Sciences（中国科学院大学）；Dalian University of Technology（大连理工大学）；UNSW Sydney（新南威尔士大学悉尼分校）；Shenzhen University of Advanced Technology（深圳理工大学）；Xiamen University（厦门大学）
InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature
Information Management Systems Institute（信息管理系统研究所）；ATHENA Research Centre（ATHENA研究中心）；Department of Informatics and Telecommunications（信息与电信系）；University of the Peloponnese（希腊皮埃里亚大学）
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
Beijing Key Laboratory of Intelligent Communication Software and Multimedia（北京智能通信软件与多媒体重点实验室）；School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（计算机学院（国家级试点软件工程学院）, 北京邮电大学）；School of Economics and Management, Beijing University of Posts and Telecommunications（经济管理学院, 北京邮电大学）
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
Panasonic Corporation（松下公司）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所（AIST））；Toyota Technological Institute（丰田工学院）
Investigating Disagreement in the Scientific Literature
Centre for Science and Technology Studies, Leiden University（莱顿大学科学技术研究中心）；SciTech Strategies, Inc.（SciTech Strategies 公司）；School of Public Policy, Georgia Institute of Technology（佐治亚理工学院公共政策学院）；School of Informatics, Computing, and Engineering, Indiana University Bloomington（印第安纳大学伯明顿分校信息学、计算与工程学院）
