We present a novel platform for evaluating the capability of Large Language Models (LLMs) to autonomously compose and critique survey papers spanning a vast array of disciplines including sciences, humanities, education, and law. Within this framework, AI systems undertake a simulated peer-review mechanism akin to traditional scholarly journals, with human organizers serving in an editorial oversight capacity. Within this framework, we organized a competition for the AutoML conference 2023. Entrants are tasked with presenting stand-alone models adept at authoring articles from designated prompts and subsequently appraising them. Assessment criteria include clarity, reference appropriateness, accountability, and the substantive value of the content. This paper presents the design of the competition, including the implementation baseline submissions and methods of evaluation.
Meow: End-to-End Outline Writing for Automatic Academic Survey
Beijing Wenge Technology Co.,Ltd（北京文格科技有限公司）；Peking University（北京大学）；Duke University（杜克大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
University of Texas at Arlington（德克萨斯理工大学）；AI Agent Lab（人工智能代理实验室）；Indiana University（印第安纳大学）；Ohio State University（俄亥俄州立大学）；Imperial College London（伦敦帝国理工学院）；Georgia Institute of Technology（佐治亚理工学院）；Appcubic（Appcubic公司）；Emory University（埃默里大学）
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Civil, Construction and Environmental Engineering, Iowa State University（土木、建设与环境工程系，爱荷华州立大学）
